Interactive visualizations of a convolutional neural network

ABSTRACT

Interactive visualizations of a convolutional neural network are provided. For example, a graphical user interface (GUI) can include a matrix having symbols indicating feature-map values that represent likelihoods of particular features being present or absent at various locations in an input to a convolutional neural network. Each column in the matrix can have feature-map values generated by convolving the input to the convolutional neural network with a respective filter for identifying a particular feature in the input. The GUI can detect, via an input device, an interaction indicating that that the columns in the matrix are to be combined into a particular number of groups. Based on the interaction, the columns can be clustered into the particular number of groups using a clustering method. The matrix in the GUI can then be updated to visually represent each respective group of columns as a single column of symbols within the matrix.

REFERENCE TO RELATED APPLICATION

This claims the benefit of priority under 35 U.S.C. § 119(e) to U.S.Provisional Patent Application No. 62/486,112, titled “VisualizingConvolutional Deep Neural Networks” and filed Apr. 17, 2017, and under35 U.S.C. § 120 as a continuation-in-part of co-pending U.S. patentapplication Ser. No. 15/584,984, titled “Visualizing Deep NeuralNetworks” and filed on May 2, 2017, which claims the benefit of priorityunder 35 U.S.C. § 119(e) to U.S. Provisional Patent Application No.62/403,944, titled “Visualizing Deep Neural Networks” and filed Oct. 4,2016, and to U.S. Provisional Patent Application No. 62/439,968, titled“Visualizing Deep Neural Networks” and filed Dec. 29, 2016, the entiretyof each of which is hereby incorporated by reference herein.

TECHNICAL FIELD

The present disclosure relates generally to operator interfaceprocessing. More specifically, but not by way of limitation, thisdisclosure relates to graphical user interfaces for visualizingconvolutional neural networks.

BACKGROUND

A neural network can be represented as one or more layers ofinterconnected “neurons” (or “nodes”) that can exchange data between oneanother. The connections between the neurons can have numeric weightsthat can be tuned based on experience. Such tuning can make neuralnetworks adaptive and capable of “learning.”

A deep neural network is a neural network that has one or more hiddenlayers of neurons between an input layer and an output layer of theneural network. Such layers between the input layer and the output layermay be referred to as “hidden” because they may not be directlyobservable in the normal functioning of the neural network. A deepneural network can include any number of hidden layers, and each hiddenlayer can include any number of neurons.

SUMMARY

One example of the present disclosure includes a system for providing aninteractive visualization of a convolutional neural network. The systemincludes a processing device and a memory device on which instructionsexecutable by the processing device are stored. The instructions cancause the processing device to display, via a display device, agraphical user interface comprising a matrix having rows and columns ofsymbols indicating feature-map values that represent likelihoods ofparticular features being present or absent at various locations in aninput to a convolutional neural network. Each column in the matrix canhave feature-map values generated by convolving the input to theconvolutional neural network with a respective filter for identifying aparticular feature in the input. The instructions can cause theprocessing device to detect, via an input device, an interaction withthe graphical user interface indicating that the columns in the matrixare to be combined into a particular number of groups. The instructionscan cause the processing device to perform operations in response todetecting the interaction. The operations can include clustering thecolumns into the particular number of groups using a clustering method.The operations can include, for each group of columns, determining aplurality of average feature-map values. Each average feature-map valuecan be determined by averaging the feature-map values represented in arespective row of the columns. The operations can include displaying anupdated version of the matrix within the graphical user interface byvisually representing each respective group of columns as a singlecolumn of symbols within the matrix. Each symbol in the single column ofsymbols can have visual characteristics representing an averagefeature-map value, from among the plurality of average feature-mapvalues, that corresponds to a row in which the symbol is positioned.

Another example of the present disclosure includes a method forproviding an interactive visualization of a convolutional neuralnetwork. The method can include displaying, via a display device, agraphical user interface comprising a matrix having rows and columns ofsymbols indicating feature-map values that represent likelihoods ofparticular features being present or absent at various locations in aninput to a convolutional neural network. Each column in the matrix canhave feature-map values generated by convolving the input to theconvolutional neural network with a respective filter for identifying aparticular feature in the input. The method can include detecting, viaan input device, an interaction with the graphical user interfaceindicating that the columns in the matrix are to be combined into aparticular number of groups. The method can include performingoperations in response to detecting the interaction. The operations caninclude clustering the columns into the particular number of groupsusing a clustering method. The operations can include, for each group ofcolumns, determining a plurality of average feature-map values. Eachaverage feature-map value can be determined by averaging the feature-mapvalues represented in a respective row of the columns. The operationscan include displaying, via the display device, an updated version ofthe matrix within the graphical user interface by visually representingeach respective group of columns as a single column of symbols withinthe matrix. Each symbol in the single column of symbols can have visualcharacteristics representing an average feature-map value, from amongthe plurality of average feature-map values, that corresponds to a rowin which the symbol is positioned. Some or all of the method can beperformed by a processing device.

Another example of the present disclosure includes a non-transitorycomputer-readable medium comprising program code that is executable by aprocessing device. The program code can cause the processing device todisplay, via a display device, a graphical user interface comprising amatrix having rows and columns of symbols indicating feature-map valuesthat represent likelihoods of particular features being present orabsent at various locations in an input to a convolutional neuralnetwork. Each column in the matrix can have feature-map values generatedby convolving the input to the convolutional neural network with arespective filter for identifying a particular feature in the input. Theprogram code can cause the processing device to detect, via an inputdevice, an interaction with the graphical user interface indicating thatthe columns in the matrix are to be combined into a particular number ofgroups. The program code can cause the processing device to performoperations in response to detecting the interaction. The operations caninclude clustering the columns into the particular number of groupsusing a clustering method. The operations can include, for each group ofcolumns, determining a plurality of average feature-map values. Eachaverage feature-map value can be determined by averaging the feature-mapvalues represented in a respective row of the columns. The operationscan include displaying an updated version of the matrix within thegraphical user interface by visually representing each respective groupof columns as a single column of symbols within the matrix. Each symbolin the single column of symbols can have visual characteristicsrepresenting an average feature-map value, from among the plurality ofaverage feature-map values, that corresponds to a row in which thesymbol is positioned.

This summary is not intended to identify key or essential features ofthe claimed subject matter, nor is it intended to be used in isolationto determine the scope of the claimed subject matter. The subject mattershould be understood by reference to appropriate portions of the entirespecification, any or all drawings, and each claim.

The foregoing, together with other features and examples, will becomemore apparent upon referring to the following specification, claims, andaccompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The patent or application file contains at least one drawing executed incolor. Copies of this patent or patent application publication withcolor drawings will be provided by the office upon request and paymentof any necessary fee. The present disclosure is described in conjunctionwith the appended figures:

FIG. 1 is a block diagram of an example of the hardware components of acomputing system according to some aspects.

FIG. 2 is an example of devices that can communicate with each otherover an exchange system and via a network according to some aspects.

FIG. 3 is a block diagram of a model of an example of a communicationsprotocol system according to some aspects.

FIG. 4 is a hierarchical diagram of an example of a communications gridcomputing system including a variety of control and worker nodesaccording to some aspects.

FIG. 5 is a flow chart of an example of a process for adjusting acommunications grid or a work project in a communications grid after afailure of a node according to some aspects.

FIG. 6 is a block diagram of a portion of a communications gridcomputing system including a control node and a worker node according tosome aspects.

FIG. 7 is a flow chart of an example of a process for executing a dataanalysis or processing project according to some aspects.

FIG. 8 is a block diagram including components of an Event StreamProcessing Engine (ESPE) according to some aspects.

FIG. 9 is a flow chart of an example of a process including operationsperformed by an event stream processing engine according to someaspects.

FIG. 10 is a block diagram of an ESP system interfacing between apublishing device and multiple event subscribing devices according tosome aspects.

FIG. 11 is an example of a graphical user interface (GUI) forvisualizing a deep neural network according to some aspects.

FIG. 12A is an example of a GUI having a node-link diagram to whichminimal thresholding has been applied according to some aspects.

FIG. 12B is an example of a GUI having a node-link diagram to whichthresholding has been applied according to some aspects.

FIG. 13 is an example of a node-link diagram according to some aspects.

FIG. 14 is an example of a quilt graph according to some aspects.

FIG. 15 is an example of a GUI having a node-link diagram in which anode has been selected according to some aspects.

FIG. 16 is an example of a GUI having a dialog box that includesinformation associated with a selected node according to some aspects.

FIG. 17 is an example of a GUI in an animation mode according to someaspects.

FIG. 18A is an example of a GUI in a comparison mode according to someaspects.

FIG. 18B is an example of the GUI in FIG. 18A in which thresholding hasbeen applied and a node has been selected according to some aspects.

FIG. 19 is an example of activated and deactivated layers of a neuralnetwork according to some aspects.

FIG. 20 is an example of merged and compressed nodes according to someaspects.

FIG. 21 is an example of a quilt graph that has been modified accordingto the activated and deactivated layers shown in FIG. 19 according tosome aspects.

FIG. 22 is a flow chart of an example of a process for visualizing adeep neural network according to some aspects.

FIG. 23 is a flow chart of an example of a process for receiving aplurality of values according to some aspects.

FIG. 24 is a flow chart of an example of a process for updating a GUIbased on user input according to some aspects.

FIG. 25 is an example of a GUI for visualizing a convolutional neuralnetwork according to some aspects.

FIG. 26 is an example of the GUI of FIG. 25 after various userinteractions according to some aspects.

FIG. 27 is an example of columns in a matrix of cells in the GUI of FIG.25 being grouped together into three clusters according to some aspects.

FIG. 28 is an example of an expanded version of two of the clusters inFIG. 27 according to some aspects.

FIG. 29 is an example of rows in a matrix of cells merged into threegroups according to some aspects.

FIG. 30 is an example of an expanded version of a group of rows in FIG.29 according to some aspects.

FIG. 31 is an example of merged or compressed nodes in a node-linkdiagram according to some aspects.

FIG. 32 is an example of activated and deactivated layers in a node-linkdiagram according to some aspects.

FIG. 33 is a flow chart of an example of a process for visualizing aconvolutional neural network according to some aspects.

FIG. 34 is a flow chart of an example of a process for determining afeature-map value according to some aspects.

FIG. 35 is a flow chart of an example of a process for interacting witha visualization of a deep neural network according to some aspects.

FIG. 36 is a flow chart of another example of a process for interactingwith a visualization of a deep neural network according to some aspects.

In the appended figures, similar components or features can have thesame reference label. Further, various components of the same type canbe distinguished by following the reference label by a dash and a secondlabel that distinguishes among the similar components. If only the firstreference label is used in the specification, the description isapplicable to any one of the similar components having the same firstreference label irrespective of the second reference label.

DETAILED DESCRIPTION

In the following description, for the purposes of explanation, specificdetails are set forth in order to provide a thorough understanding ofexamples of the technology. But various examples can be practicedwithout these specific details. The figures and description are notintended to be restrictive.

The ensuing description provides examples only, and is not intended tolimit the scope, applicability, or configuration of the disclosure.Rather, the ensuing description of the examples provides those skilledin the art with an enabling description for implementing an example.Various changes may be made in the function and arrangement of elementswithout departing from the spirit and scope of the technology as setforth in the appended claims.

Specific details are given in the following description to provide athorough understanding of the examples. But the examples may bepracticed without these specific details. For example, circuits,systems, networks, processes, and other components can be shown ascomponents in block diagram form to prevent obscuring the examples inunnecessary detail. In other examples, well-known circuits, processes,algorithms, structures, and techniques may be shown without unnecessarydetail in order to avoid obscuring the examples.

Also, individual examples can be described as a process that is depictedas a flowchart, a flow diagram, a data flow diagram, a structurediagram, or a block diagram. Although a flowchart can describe theoperations as a sequential process, many of the operations can beperformed in parallel or concurrently. In addition, the order of theoperations can be re-arranged. A process is terminated when itsoperations are completed, but can have additional operations notincluded in a figure. A process can correspond to a method, a function,a procedure, a subroutine, a subprogram, etc. When a process correspondsto a function, its termination can correspond to a return of thefunction to the calling function or the main function.

Systems depicted in some of the figures can be provided in variousconfigurations. In some examples, the systems can be configured as adistributed system where one or more components of the system aredistributed across one or more networks in a cloud computing system.

Certain aspects and features of the present disclosure relate to agraphical user interface (GUI) for visualizing a deep neural network.The GUI can include a node-link diagram that visually represents nodes(neurons) in the deep neural network and connections (links) between thenodes. The GUI can additionally or alternatively include a quilt graphhaving a matrix of cells indicating connections between nodes in thedeep neural network. The node-link diagram and the quilt graph can becolor coded to express information about the nodes and connections inthe deep neural network. For example, objects representing nodes andconnections in the node-link diagram and the quilt graph can be coloredvarious shades of red or blue to express the activation values of thenodes and connections in the deep neural network. The GUI can alsoprovide thresholding, node inspection, and tooltip operations thatenable a user to customize the amount of visual information displayed bythe GUI.

In some examples, the GUI can have multiple operational modes—forinstance, an input mode, an animation mode, and a comparison mode. Theinput mode can enable a user to provide an input to the deep neuralnetwork and visualize how the deep neural network responds to the input.The animation mode can provide multiple inputs to the deep neuralnetwork, aggregate values associated with the nodes and connectionsresulting from the multiple inputs, and display the aggregate values inthe node-link diagram and the quilt graph. The comparison mode canenable a user to compare a first set of values resulting from one inputinto the deep neural network against a second set of values resultingfrom another input into the deep neural network. For example, thecomparison mode can determine a difference between the first set ofvalues and the second set of values and display the difference in thenode-link diagram and the quilt graph.

In some examples, the GUI can be tailored for presenting informationabout a convolutional neural network, which can be a type of deep neuralnetwork. More specifically, a convolutional neural network can be a deepneural network that includes a convolutional layer for performingconvolution operations on an input to the convolutional neural network.The convolutional layer can include one or more filters (which can alsobe referred to as “kernels”) for identifying features in the input. Afilter can be a two-dimensional matrix of weights that can be trained ortuned. During a forward pass, each of the filters slides across (e.g.,is convolved with) an input to the convolutional neural network. As afilter slides over the input, dot products are computed between thefilter's weights and each position in the input. This can result in afeature map that includes the filter's responses at every spatialposition in the input. This process can be repeated for each filter toproduce separate feature maps for each filter. The convolutional neuralnetwork can learn which filters activate when the filter “sees” aparticular feature in the input. For example, the convolutional neuralnetwork can learn that a certain filter actives when there is aparticular visual feature in an input image. Some GUIs of the presentdisclosure can display information related to the convolutionallayer(s), filter(s), feature maps, or any combination of these. Theconvolutional neural network can also include a pooling layer, a set offully connected layers (e.g., that form a feed forward neural network),or both. Some examples of the present disclosure can include graphicalinformation related to the pooling layer, the set of fully connectedlayers, or both.

In some examples, the abovementioned features can enable a user todiscover and explore characteristics of a deep neural network that arenot well understood currently. Presently, deep neural networks are oftenconsidered a “black box.” For example, although the procedure to trainand use deep neural networks may be known, a deeper understanding of theinner workings of deep neural networks is often lacking. And, as deepneural networks grow in size, more parameters accumulate and it canbecome more difficult to understand what the deep neural network isdoing to produce its final results. But some examples of the presentdisclosure provide an intuitive, easy-to-use GUI that can enable usersto obtain a better understanding of how a deep neural network isoperating, why the deep neural network is making certain decisions, andhow the deep neural network produces final results. This may lead to abetter understanding of how to train and build deep neural networks thatare more efficient, robust, and accurate. For example, informationdisplayed in the GUI may enable a designer to determine: (i) how a deepneural network returns a certain output; (ii) how input values movethrough nodes and links in the deep neural network to generate theoutput; and (iii) how changing the values of the input affect movementthrough the deep neural network and the output value returned. In oneparticular example, a designer of a deep neural network can reviewhidden-layer activations for patterns that relate to concepts to belearned by the deep neural network. If the designer fails to recognizeany patterns in the hidden-layer activations, it may indicate that thedeep neural network is simply storing data by rote memorization, ratherthan learning the deeper concepts. The designer may be able to addressthis issue by, for example, supplying more training data to the deepneural network or simplifying the deep neural network. But without thevisualizations provided in some examples of the present disclosure, thedesigner may be unable to properly identify the issue.

In some examples, information displayed in the GUI may enable a designerof a deep neural network to optimize the deep neural network to reduce(i) the number of processing cycles executed by the deep neural network,(ii) the amount of memory consumed by the deep neural network, (iii) theamount of memory accesses performed by the deep neural network, (iv) orany combination of these. As a particular example, a designer of a deepneural network can use the GUI to determine that certain hidden layers(or nodes) produce repetitive results or are otherwise extraneous. So,the designer can remove these hidden layers (or nodes) to reduce theamount of unnecessary processing that is performed by the neuralnetwork.

Some examples of the present disclosure can use specific rules thatrender information in a specific format that is then used and applied tocreate the GUI. For example, in the animation mode, the computing devicecan use specific rules that (i) generate different states in a deepneural network as a function of multiple different inputs; (ii)aggregate information from the different states of the neural networkinto aggregated data; and (iii) apply transformations to the aggregateddata to generate output data. The output data can then be used togenerate the GUI. As another example, in the comparison mode, thecomputing device can use specific rules that (i) generate two differentstates in a deep neural network as a function of two different inputs;(ii) compare information from the different states of the neural networkto determine comparison data; (iii) apply transformations to thecomparison data to generate output data. The output data can then beused to generate the GUI. In some examples, the transformations of theaggregated data (or the comparison data) can include multiplying,dividing, subtracting, or otherwise manipulating portions of theaggregated data (or the comparison data).

In some examples, a deep neural network can be represented by specificrules. For example, a set of rules can define outputs of the deep neuralnetwork as a function of inputs and weights corresponding nodes andconnections in the deep neural network. Respective values for the nodesin the deep neural network can be generated by evaluating an inputagainst the set of rules. These values can be referred to asintermediate values. Respective values for the connections between thenodes in the deep neural network can also be generated by evaluating theinput against the set of rules. These values can be transition values. Afinal output of the deep neural network can be generated from theintermediate values and the transition values. The intermediate values,transition values, final output, or any combination of these can berendered in the GUI.

FIGS. 1-10 depict examples of systems and methods usable for visualizingdeep neural networks according to some aspects. For example, FIG. 1 is ablock diagram of an example of the hardware components of a computingsystem according to some aspects. Data transmission network 100 is aspecialized computer system that may be used for processing largeamounts of data where a large number of computer processing cycles arerequired.

Data transmission network 100 may also include computing environment114. Computing environment 114 may be a specialized computer or othermachine that processes the data received within the data transmissionnetwork 100. The computing environment 114 may include one or more othersystems. For example, computing environment 114 may include a databasesystem 118 or a communications grid 120. The computing environment 114can include one or more processing devices (e.g., distributed over oneor more networks or otherwise in communication with one another) that,in some examples, can collectively be referred to as a processor or aprocessing device.

Data transmission network 100 also includes one or more network devices102. Network devices 102 may include client devices that can communicatewith computing environment 114. For example, network devices 102 maysend data to the computing environment 114 to be processed, may sendcommunications to the computing environment 114 to control differentaspects of the computing environment or the data it is processing, amongother reasons. Network devices 102 may interact with the computingenvironment 114 through a number of ways, such as, for example, over oneor more networks 108.

In some examples, network devices 102 may provide a large amount ofdata, either all at once or streaming over a period of time (e.g., usingevent stream processing (ESP)), to the computing environment 114 vianetworks 108. For example, the network devices 102 can transmitelectronic messages (e.g., for use in visualizing deep neural networks)all at once or streaming over a period of time, to the computingenvironment 114 via networks 108.

The network devices 102 may include network computers, sensors,databases, or other devices that may transmit or otherwise provide datato computing environment 114. For example, network devices 102 mayinclude local area network devices, such as routers, hubs, switches, orother computer networking devices. These devices may provide a varietyof stored or generated data, such as network data or data specific tothe network devices 102 themselves. Network devices 102 may also includesensors that monitor their environment or other devices to collect dataregarding that environment or those devices, and such network devices102 may provide data they collect over time. Network devices 102 mayalso include devices within the internet of things, such as deviceswithin a home automation network. Some of these devices may be referredto as edge devices, and may involve edge-computing circuitry. Data maybe transmitted by network devices 102 directly to computing environment114 or to network-attached data stores, such as network-attached datastores 110 for storage so that the data may be retrieved later by thecomputing environment 114 or other portions of data transmission network100. For example, the network devices 102 can transmit data usable forvisualizing deep neural networks to a network-attached data store 110for storage. The computing environment 114 may later retrieve the datafrom the network-attached data store 110 and use the data to generate aGUI for visualizing deep neural networks.

Network-attached data stores 110 can store data to be processed by thecomputing environment 114 as well as any intermediate or final datagenerated by the computing system in non-volatile memory. But in certainexamples, the configuration of the computing environment 114 allows itsoperations to be performed such that intermediate and final data resultscan be stored solely in volatile memory (e.g., RAM), without arequirement that intermediate or final data results be stored tonon-volatile types of memory (e.g., disk). This can be useful in certainsituations, such as when the computing environment 114 receives ad hocqueries from a user and when responses, which are generated byprocessing large amounts of data, need to be generated dynamically(e.g., on the fly). In this situation, the computing environment 114 maybe configured to retain the processed information within memory so thatresponses can be generated for the user at different levels of detail aswell as allow a user to interactively query against this information.

Network-attached data stores 110 may store a variety of different typesof data organized in a variety of different ways and from a variety ofdifferent sources. For example, network-attached data stores may includestorage other than primary storage located within computing environment114 that is directly accessible by processors located therein.Network-attached data stores may include secondary, tertiary orauxiliary storage, such as large hard drives, servers, virtual memory,among other types. Storage devices may include portable or non-portablestorage devices, optical storage devices, and various other mediumscapable of storing, containing data. A machine-readable storage mediumor computer-readable storage medium may include a non-transitory mediumin which data can be stored and that does not include carrier waves ortransitory electronic communications. Examples of a non-transitorymedium may include, for example, a magnetic disk or tape, opticalstorage media such as compact disk or digital versatile disk, flashmemory, memory or memory devices. A computer-program product may includecode or machine-executable instructions that may represent a procedure,a function, a subprogram, a program, a routine, a subroutine, a module,a software package, a class, or any combination of instructions, datastructures, or program statements. A code segment may be coupled toanother code segment or a hardware circuit by passing or receivinginformation, data, arguments, parameters, or memory contents.Information, arguments, parameters, data, etc. may be passed, forwarded,or transmitted via any suitable means including memory sharing, messagepassing, token passing, network transmission, among others. Furthermore,the data stores may hold a variety of different types of data. Forexample, network-attached data stores 110 may hold unstructured (e.g.,raw) data.

The unstructured data may be presented to the computing environment 114in different forms such as a flat file or a conglomerate of datarecords, and may have data values and accompanying time stamps. Thecomputing environment 114 may be used to analyze the unstructured datain a variety of ways to determine the best way to structure (e.g.,hierarchically) that data, such that the structured data is tailored toa type of further analysis that a user wishes to perform on the data.For example, after being processed, the unstructured time-stamped datamay be aggregated by time (e.g., into daily time period units) togenerate time series data or structured hierarchically according to oneor more dimensions (e.g., parameters, attributes, or variables). Forexample, data may be stored in a hierarchical data structure, such as arelational online analytical processing (ROLAP) or multidimensionalonline analytical processing (MOLAP) database, or may be stored inanother tabular form, such as in a flat-hierarchy form.

Data transmission network 100 may also include one or more server farms106. Computing environment 114 may route select communications or datato the sever farms 106 or one or more servers within the server farms106. Server farms 106 can be configured to provide information in apredetermined manner. For example, server farms 106 may access data totransmit in response to a communication. Server farms 106 may beseparately housed from each other device within data transmissionnetwork 100, such as computing environment 114, or may be part of adevice or system.

Server farms 106 may host a variety of different types of dataprocessing as part of data transmission network 100. Server farms 106may receive a variety of different data from network devices, fromcomputing environment 114, from cloud network 116, or from othersources. The data may have been obtained or collected from one or morewebsites, sensors, as inputs from a control database, or may have beenreceived as inputs from an external system or device. Server farms 106may assist in processing the data by turning raw data into processeddata based on one or more rules implemented by the server farms. Forexample, sensor data may be analyzed to determine changes in anenvironment over time or in real-time.

Data transmission network 100 may also include one or more cloudnetworks 116. Cloud network 116 may include a cloud infrastructuresystem that provides cloud services. In certain examples, servicesprovided by the cloud network 116 may include a host of services thatare made available to users of the cloud infrastructure system ondemand. Cloud network 116 is shown in FIG. 1 as being connected tocomputing environment 114 (and therefore having computing environment114 as its client or user), but cloud network 116 may be connected to orutilized by any of the devices in FIG. 1. Services provided by the cloudnetwork 116 can dynamically scale to meet the needs of its users. Thecloud network 116 may include one or more computers, servers, orsystems. In some examples, the computers, servers, or systems that makeup the cloud network 116 are different from the user's own on-premisescomputers, servers, or systems. For example, the cloud network 116 mayhost an application, and a user may, via a communication network such asthe Internet, order and use the application on demand. In some examples,the cloud network 116 may host an application for visualizing deepneural networks.

While each device, server, and system in FIG. 1 is shown as a singledevice, multiple devices may instead be used. For example, a set ofnetwork devices can be used to transmit various communications from asingle user, or remote server 140 may include a server stack. As anotherexample, data may be processed as part of computing environment 114.

Each communication within data transmission network 100 (e.g., betweenclient devices, between a device and connection management system 150,between server farms 106 and computing environment 114, or between aserver and a device) may occur over one or more networks 108. Networks108 may include one or more of a variety of different types of networks,including a wireless network, a wired network, or a combination of awired and wireless network. Examples of suitable networks include theInternet, a personal area network, a local area network (LAN), a widearea network (WAN), or a wireless local area network (WLAN). A wirelessnetwork may include a wireless interface or combination of wirelessinterfaces. As an example, a network in the one or more networks 108 mayinclude a short-range communication channel, such as a Bluetooth or aBluetooth Low Energy channel. A wired network may include a wiredinterface. The wired or wireless networks may be implemented usingrouters, access points, bridges, gateways, or the like, to connectdevices in the network 108. The networks 108 can be incorporatedentirely within or can include an intranet, an extranet, or acombination thereof. In one example, communications between two or moresystems or devices can be achieved by a secure communications protocol,such as secure sockets layer (SSL) or transport layer security (TLS). Inaddition, data or transactional details may be encrypted.

Some aspects may utilize the Internet of Things (IoT), where things(e.g., machines, devices, phones, sensors) can be connected to networksand the data from these things can be collected and processed within thethings or external to the things. For example, the IoT can includesensors in many different devices, and high value analytics can beapplied to identify hidden relationships and drive increasedefficiencies. This can apply to both big data analytics and real-time(e.g., ESP) analytics.

As noted, computing environment 114 may include a communications grid120 and a transmission network database system 118. Communications grid120 may be a grid-based computing system for processing large amounts ofdata. The transmission network database system 118 may be for managing,storing, and retrieving large amounts of data that are distributed toand stored in the one or more network-attached data stores 110 or otherdata stores that reside at different locations within the transmissionnetwork database system 118. The computing nodes in the communicationsgrid 120 and the transmission network database system 118 may share thesame processor hardware, such as processors that are located withincomputing environment 114.

In some examples, the computing environment 114, a network device 102,or both can implement one or more processes for visualizing deep neuralnetworks. For example, the computing environment 114, a network device102, or both can implement one or more versions of the processesdiscussed with respect to FIGS. 22-24.

FIG. 2 is an example of devices that can communicate with each otherover an exchange system and via a network according to some aspects. Asnoted, each communication within data transmission network 100 may occurover one or more networks. System 200 includes a network device 204configured to communicate with a variety of types of client devices, forexample client devices 230, over a variety of types of communicationchannels.

As shown in FIG. 2, network device 204 can transmit a communication overa network (e.g., a cellular network via a base station 210). In someexamples, the communication can include times series data. Thecommunication can be routed to another network device, such as networkdevices 205-209, via base station 210. The communication can also berouted to computing environment 214 via base station 210. In someexamples, the network device 204 may collect data either from itssurrounding environment or from other network devices (such as networkdevices 205-209) and transmit that data to computing environment 214.

Although network devices 204-209 are shown in FIG. 2 as a mobile phone,laptop computer, tablet computer, temperature sensor, motion sensor, andaudio sensor respectively, the network devices may be or include sensorsthat are sensitive to detecting aspects of their environment. Forexample, the network devices may include sensors such as water sensors,power sensors, electrical current sensors, chemical sensors, opticalsensors, pressure sensors, geographic or position sensors (e.g., GPS),velocity sensors, acceleration sensors, flow rate sensors, among others.Examples of characteristics that may be sensed include force, torque,load, strain, position, temperature, air pressure, fluid flow, chemicalproperties, resistance, electromagnetic fields, radiation, irradiance,proximity, acoustics, moisture, distance, speed, vibrations,acceleration, electrical potential, and electrical current, amongothers. The sensors may be mounted to various components used as part ofa variety of different types of systems. The network devices may detectand record data related to the environment that it monitors, andtransmit that data to computing environment 214.

The network devices 204-209 may also perform processing on data itcollects before transmitting the data to the computing environment 214,or before deciding whether to transmit data to the computing environment214. For example, network devices 204-209 may determine whether datacollected meets certain rules, for example by comparing data or valuescalculated from the data and comparing that data to one or morethresholds. The network devices 204-209 may use this data or comparisonsto determine if the data is to be transmitted to the computingenvironment 214 for further use or processing. In some examples, thenetwork devices 204-209 can pre-process the data prior to transmittingthe data to the computing environment 214. For example, the networkdevices 204-209 can reformat the data before transmitting the data tothe computing environment 214 for further processing (e.g., analyzingthe data to generate a GUI for visualizing deep neural networks).

Computing environment 214 may include machines 220, 240. Althoughcomputing environment 214 is shown in FIG. 2 as having two machines 220,240, computing environment 214 may have only one machine or may havemore than two machines. The machines 220, 240 that make up computingenvironment 214 may include specialized computers, servers, or othermachines that are configured to individually or collectively processlarge amounts of data. The computing environment 214 may also includestorage devices that include one or more databases of structured data,such as data organized in one or more hierarchies, or unstructured data.The databases may communicate with the processing devices withincomputing environment 214 to distribute data to them. Since networkdevices may transmit data to computing environment 214, that data may bereceived by the computing environment 214 and subsequently stored withinthose storage devices. Data used by computing environment 214 may alsobe stored in data stores 235, which may also be a part of or connectedto computing environment 214.

Computing environment 214 can communicate with various devices via oneor more routers 225 or other inter-network or intra-network connectioncomponents. For example, computing environment 214 may communicate withclient devices 230 via one or more routers 225. Computing environment214 may collect, analyze or store data from or pertaining tocommunications, client device operations, client rules, oruser-associated actions stored at one or more data stores 235. Such datamay influence communication routing to the devices within computingenvironment 214, how data is stored or processed within computingenvironment 214, among other actions.

Notably, various other devices can further be used to influencecommunication routing or processing between devices within computingenvironment 214 and with devices outside of computing environment 214.For example, as shown in FIG. 2, computing environment 214 may include amachine 240 that is a web server. Computing environment 214 can retrievedata of interest, such as client information (e.g., product information,client rules, etc.), technical product details, news, blog posts,e-mails, forum posts, electronic documents, social media posts (e.g.,Twitter™ posts or Facebook™ posts), time series data, and so on.

In addition to computing environment 214 collecting data (e.g., asreceived from network devices, such as sensors, and client devices orother sources) to be processed as part of a big data analytics project,it may also receive data in real time as part of a streaming analyticsenvironment. As noted, data may be collected using a variety of sourcesas communicated via different kinds of networks or locally. Such datamay be received on a real-time streaming basis. For example, networkdevices 204-209 may receive data periodically and in real time from aweb server or other source. Devices within computing environment 214 mayalso perform pre-analysis on data it receives to determine if the datareceived should be processed as part of an ongoing project. For example,as part of a project in which a visualization of a deep neural networkis generated from data, the computing environment 214 can perform apre-analysis of the data. The pre-analysis can include determiningwhether the data is in a correct format for the visualization and, ifnot, reformatting the data into the correct format.

FIG. 3 is a block diagram of a model of an example of a communicationsprotocol system according to some aspects. More specifically, FIG. 3identifies operation of a computing environment in an Open SystemsInteraction model that corresponds to various connection components. Themodel 300 shows, for example, how a computing environment, such ascomputing environment (or computing environment 214 in FIG. 2) maycommunicate with other devices in its network, and control howcommunications between the computing environment and other devices areexecuted and under what conditions.

The model 300 can include layers 302-314. The layers 302-314 arearranged in a stack. Each layer in the stack serves the layer one levelhigher than it (except for the application layer, which is the highestlayer), and is served by the layer one level below it (except for thephysical layer 302, which is the lowest layer). The physical layer 302is the lowest layer because it receives and transmits raw bites of data,and is the farthest layer from the user in a communications system. Onthe other hand, the application layer is the highest layer because itinteracts directly with a software application.

As noted, the model 300 includes a physical layer 302. Physical layer302 represents physical communication, and can define parameters of thatphysical communication. For example, such physical communication maycome in the form of electrical, optical, or electromagneticcommunications. Physical layer 302 also defines protocols that maycontrol communications within a data transmission network.

Link layer 304 defines links and mechanisms used to transmit (e.g.,move) data across a network. The link layer manages node-to-nodecommunications, such as within a grid-computing environment. Link layer304 can detect and correct errors (e.g., transmission errors in thephysical layer 302). Link layer 304 can also include a media accesscontrol (MAC) layer and logical link control (LLC) layer.

Network layer 306 can define the protocol for routing within a network.In other words, the network layer coordinates transferring data acrossnodes in a same network (e.g., such as a grid-computing environment).Network layer 306 can also define the processes used to structure localaddressing within the network.

Transport layer 308 can manage the transmission of data and the qualityof the transmission or receipt of that data. Transport layer 308 canprovide a protocol for transferring data, such as, for example, aTransmission Control Protocol (TCP). Transport layer 308 can assembleand disassemble data frames for transmission. The transport layer canalso detect transmission errors occurring in the layers below it.

Session layer 310 can establish, maintain, and manage communicationconnections between devices on a network. In other words, the sessionlayer controls the dialogues or nature of communications between networkdevices on the network. The session layer may also establishcheckpointing, adjournment, termination, and restart procedures.

Presentation layer 312 can provide translation for communicationsbetween the application and network layers. In other words, this layermay encrypt, decrypt or format data based on data types known to beaccepted by an application or network layer.

Application layer 314 interacts directly with software applications andend users, and manages communications between them. Application layer314 can identify destinations, local resource states or availability orcommunication content or formatting using the applications.

For example, a communication link can be established between two deviceson a network. One device can transmit an analog or digitalrepresentation of an electronic message that includes a data set to theother device. The other device can receive the analog or digitalrepresentation at the physical layer 302. The other device can transmitthe data associated with the electronic message through the remaininglayers 304-314. The application layer 314 can receive data associatedwith the electronic message. The application layer 314 can identify oneor more applications, such as an application for visualizing deep neuralnetworks, to which to transmit data associated with the electronicmessage. The application layer 314 can transmit the data to theidentified application.

Intra-network connection components 322, 324 can operate in lowerlevels, such as physical layer 302 and link layer 304, respectively. Forexample, a hub can operate in the physical layer, a switch can operatein the physical layer, and a router can operate in the network layer.Inter-network connection components 326, 328 are shown to operate onhigher levels, such as layers 306-314. For example, routers can operatein the network layer and network devices can operate in the transport,session, presentation, and application layers.

A computing environment 330 can interact with or operate on, in variousexamples, one, more, all or any of the various layers. For example,computing environment 330 can interact with a hub (e.g., via the linklayer) to adjust which devices the hub communicates with. The physicallayer 302 may be served by the link layer 304, so it may implement suchdata from the link layer 304. For example, the computing environment 330may control which devices from which it can receive data. For example,if the computing environment 330 knows that a certain network device hasturned off, broken, or otherwise become unavailable or unreliable, thecomputing environment 330 may instruct the hub to prevent any data frombeing transmitted to the computing environment 330 from that networkdevice. Such a process may be beneficial to avoid receiving data that isinaccurate or that has been influenced by an uncontrolled environment.As another example, computing environment 330 can communicate with abridge, switch, router or gateway and influence which device within thesystem (e.g., system 200) the component selects as a destination. Insome examples, computing environment 330 can interact with variouslayers by exchanging communications with equipment operating on aparticular layer by routing or modifying existing communications. Inanother example, such as in a grid-computing environment, a node maydetermine how data within the environment should be routed (e.g., whichnode should receive certain data) based on certain parameters orinformation provided by other layers within the model.

The computing environment 330 may be a part of a communications gridenvironment, the communications of which may be implemented as shown inthe protocol of FIG. 3. For example, referring back to FIG. 2, one ormore of machines 220 and 240 may be part of a communicationsgrid-computing environment. A gridded computing environment may beemployed in a distributed system with non-interactive workloads wheredata resides in memory on the machines, or compute nodes. In such anenvironment, analytic code, instead of a database management system, cancontrol the processing performed by the nodes. Data is co-located bypre-distributing it to the grid nodes, and the analytic code on eachnode loads the local data into memory. Each node may be assigned aparticular task, such as a portion of a processing project, or toorganize or control other nodes within the grid. For example, each nodemay be assigned a portion of a processing task for generating a GUI forvisualizing deep neural networks.

FIG. 4 is a hierarchical diagram of an example of a communications gridcomputing system 400 including a variety of control and worker nodesaccording to some aspects. Communications grid computing system 400includes three control nodes and one or more worker nodes.Communications grid computing system 400 includes control nodes 402,404, and 406. The control nodes are communicatively connected viacommunication paths 451, 453, and 455. The control nodes 402-406 maytransmit information (e.g., related to the communications grid ornotifications) to and receive information from each other. Althoughcommunications grid computing system 400 is shown in FIG. 4 as includingthree control nodes, the communications grid may include more or lessthan three control nodes.

Communications grid computing system 400 (which can be referred to as a“communications grid”) also includes one or more worker nodes. Shown inFIG. 4 are six worker nodes 410-420. Although FIG. 4 shows six workernodes, a communications grid can include more or less than six workernodes. The number of worker nodes included in a communications grid maybe dependent upon how large the project or data set is being processedby the communications grid, the capacity of each worker node, the timedesignated for the communications grid to complete the project, amongothers. Each worker node within the communications grid computing system400 may be connected (wired or wirelessly, and directly or indirectly)to control nodes 402-406. Each worker node may receive information fromthe control nodes (e.g., an instruction to perform work on a project)and may transmit information to the control nodes (e.g., a result fromwork performed on a project). Furthermore, worker nodes may communicatewith each other directly or indirectly. For example, worker nodes maytransmit data between each other related to a job being performed or anindividual task within a job being performed by that worker node. Insome examples, worker nodes may not be connected (communicatively orotherwise) to certain other worker nodes. For example, a worker node 410may only be able to communicate with a particular control node 402. Theworker node 410 may be unable to communicate with other worker nodes412-420 in the communications grid, even if the other worker nodes412-420 are controlled by the same control node 402.

A control node 402-406 may connect with an external device with whichthe control node 402-406 may communicate (e.g., a communications griduser, such as a server or computer, may connect to a controller of thegrid). For example, a server or computer may connect to control nodes402-406 and may transmit a project or job to the node, such as a projector job related to visualizing deep neural networks. The project mayinclude the data set. Once the control node 402-406 receives such aproject including a large data set, the control node may distribute thedata set or projects related to the data set to be performed by workernodes. Alternatively, for a project including a large data set, the dataset may be receive or stored by a machine other than a control node402-406 (e.g., a Hadoop data node).

Control nodes 402-406 can maintain knowledge of the status of the nodesin the grid (e.g., grid status information), accept work requests fromclients, subdivide the work across worker nodes, and coordinate theworker nodes, among other responsibilities. Worker nodes 412-420 mayaccept work requests from a control node 402-406 and provide the controlnode with results of the work performed by the worker node. A grid maybe started from a single node (e.g., a machine, computer, server, etc.).This first node may be assigned or may start as the primary control node402 that will control any additional nodes that enter the grid.

When a project is submitted for execution (e.g., by a client or acontroller of the grid) it may be assigned to a set of nodes. After thenodes are assigned to a project, a data structure (e.g., a communicator)may be created. The communicator may be used by the project forinformation to be shared between the project code running on each node.A communication handle may be created on each node. A handle, forexample, is a reference to the communicator that is valid within asingle process on a single node, and the handle may be used whenrequesting communications between nodes.

A control node, such as control node 402, may be designated as theprimary control node. A server, computer or other external device mayconnect to the primary control node. Once the control node 402 receivesa project, the primary control node may distribute portions of theproject to its worker nodes for execution. For example, a project forvisualizing a deep neural network can be initiated on communicationsgrid computing system 400. A primary control node can control the workto be performed for the project in order to complete the project asrequested or instructed. The primary control node may distribute work tothe worker nodes 412-420 based on various factors, such as which subsetsor portions of projects may be completed most efficiently and in thecorrect amount of time. For example, a worker node 412 may generation avisualization using at least a portion of data that is already local(e.g., stored on) the worker node. The primary control node alsocoordinates and processes the results of the work performed by eachworker node 412-420 after each worker node 412-420 executes andcompletes its job. For example, the primary control node may receive aresult from one or more worker nodes 412-420, and the primary controlnode may organize (e.g., collect and assemble) the results received andcompile them to produce a complete result for the project received fromthe end user.

Any remaining control nodes, such as control nodes 404, 406, may beassigned as backup control nodes for the project. In an example, backupcontrol nodes may not control any portion of the project. Instead,backup control nodes may serve as a backup for the primary control nodeand take over as primary control node if the primary control node wereto fail. If a communications grid were to include only a single controlnode 402, and the control node 402 were to fail (e.g., the control nodeis shut off or breaks) then the communications grid as a whole may failand any project or job being run on the communications grid may fail andmay not complete. While the project may be run again, such a failure maycause a delay (severe delay in some cases, such as overnight delay) incompletion of the project. Therefore, a grid with multiple control nodes402-406, including a backup control node, may be beneficial.

In some examples, the primary control node may open a pair of listeningsockets to add another node or machine to the grid. A socket may be usedto accept work requests from clients, and the second socket may be usedto accept connections from other grid nodes. The primary control nodemay be provided with a list of other nodes (e.g., other machines,computers, servers, etc.) that can participate in the grid, and the rolethat each node can fill in the grid. Upon startup of the primary controlnode (e.g., the first node on the grid), the primary control node mayuse a network protocol to start the server process on every other nodein the grid. Command line parameters, for example, may inform each nodeof one or more pieces of information, such as: the role that the nodewill have in the grid, the host name of the primary control node, theport number on which the primary control node is accepting connectionsfrom peer nodes, among others. The information may also be provided in aconfiguration file, transmitted over a secure shell tunnel, recoveredfrom a configuration server, among others. While the other machines inthe grid may not initially know about the configuration of the grid,that information may also be sent to each other node by the primarycontrol node. Updates of the grid information may also be subsequentlysent to those nodes.

For any control node other than the primary control node added to thegrid, the control node may open three sockets. The first socket mayaccept work requests from clients, the second socket may acceptconnections from other grid members, and the third socket may connect(e.g., permanently) to the primary control node. When a control node(e.g., primary control node) receives a connection from another controlnode, it first checks to see if the peer node is in the list ofconfigured nodes in the grid. If it is not on the list, the control nodemay clear the connection. If it is on the list, it may then attempt toauthenticate the connection. If authentication is successful, theauthenticating node may transmit information to its peer, such as theport number on which a node is listening for connections, the host nameof the node, information about how to authenticate the node, among otherinformation. When a node, such as the new control node, receivesinformation about another active node, it can check to see if it alreadyhas a connection to that other node. If it does not have a connection tothat node, it may then establish a connection to that control node.

Any worker node added to the grid may establish a connection to theprimary control node and any other control nodes on the grid. Afterestablishing the connection, it may authenticate itself to the grid(e.g., any control nodes, including both primary and backup, or a serveror user controlling the grid). After successful authentication, theworker node may accept configuration information from the control node.

When a node joins a communications grid (e.g., when the node is poweredon or connected to an existing node on the grid or both), the node isassigned (e.g., by an operating system of the grid) a universally uniqueidentifier (UUID). This unique identifier may help other nodes andexternal entities (devices, users, etc.) to identify the node anddistinguish it from other nodes. When a node is connected to the grid,the node may share its unique identifier with the other nodes in thegrid. Since each node may share its unique identifier, each node mayknow the unique identifier of every other node on the grid. Uniqueidentifiers may also designate a hierarchy of each of the nodes (e.g.,backup control nodes) within the grid. For example, the uniqueidentifiers of each of the backup control nodes may be stored in a listof backup control nodes to indicate an order in which the backup controlnodes will take over for a failed primary control node to become a newprimary control node. But, a hierarchy of nodes may also be determinedusing methods other than using the unique identifiers of the nodes. Forexample, the hierarchy may be predetermined, or may be assigned based onother predetermined factors.

The grid may add new machines at any time (e.g., initiated from anycontrol node). Upon adding a new node to the grid, the control node mayfirst add the new node to its table of grid nodes. The control node mayalso then notify every other control node about the new node. The nodesreceiving the notification may acknowledge that they have updated theirconfiguration information.

Primary control node 402 may, for example, transmit one or morecommunications to backup control nodes 404, 406 (and, for example, toother control or worker nodes 412-420 within the communications grid).Such communications may be sent periodically, at fixed time intervals,between known fixed stages of the project's execution, among otherprotocols. The communications transmitted by primary control node 402may be of varied types and may include a variety of types ofinformation. For example, primary control node 402 may transmitsnapshots (e.g., status information) of the communications grid so thatbackup control node 404 always has a recent snapshot of thecommunications grid. The snapshot or grid status may include, forexample, the structure of the grid (including, for example, the workernodes 410-420 in the communications grid, unique identifiers of theworker nodes 410-420, or their relationships with the primary controlnode 402) and the status of a project (including, for example, thestatus of each worker node's portion of the project). The snapshot mayalso include analysis or results received from worker nodes 410-420 inthe communications grid. The backup control nodes 404, 406 may receiveand store the backup data received from the primary control node 402.The backup control nodes 404, 406 may transmit a request for such asnapshot (or other information) from the primary control node 402, orthe primary control node 402 may send such information periodically tothe backup control nodes 404, 406.

As noted, the backup data may allow a backup control node 404, 406 totake over as primary control node if the primary control node 402 failswithout requiring the communications grid to start the project over fromscratch. If the primary control node 402 fails, the backup control node404, 406 that will take over as primary control node may retrieve themost recent version of the snapshot received from the primary controlnode 402 and use the snapshot to continue the project from the stage ofthe project indicated by the backup data. This may prevent failure ofthe project as a whole.

A backup control node 404, 406 may use various methods to determine thatthe primary control node 402 has failed. In one example of such amethod, the primary control node 402 may transmit (e.g., periodically) acommunication to the backup control node 404, 406 that indicates thatthe primary control node 402 is working and has not failed, such as aheartbeat communication. The backup control node 404, 406 may determinethat the primary control node 402 has failed if the backup control nodehas not received a heartbeat communication for a certain predeterminedperiod of time. Alternatively, a backup control node 404, 406 may alsoreceive a communication from the primary control node 402 itself (beforeit failed) or from a worker node 410-420 that the primary control node402 has failed, for example because the primary control node 402 hasfailed to communicate with the worker node 410-420.

Different methods may be performed to determine which backup controlnode of a set of backup control nodes (e.g., backup control nodes 404,406) can take over for failed primary control node 402 and become thenew primary control node. For example, the new primary control node maybe chosen based on a ranking or “hierarchy” of backup control nodesbased on their unique identifiers. In an alternative example, a backupcontrol node may be assigned to be the new primary control node byanother device in the communications grid or from an external device(e.g., a system infrastructure or an end user, such as a server orcomputer, controlling the communications grid). In another alternativeexample, the backup control node that takes over as the new primarycontrol node may be designated based on bandwidth or other statisticsabout the communications grid.

A worker node within the communications grid may also fail. If a workernode fails, work being performed by the failed worker node may beredistributed amongst the operational worker nodes. In an alternativeexample, the primary control node may transmit a communication to eachof the operable worker nodes still on the communications grid that eachof the worker nodes should purposefully fail also. After each of theworker nodes fail, they may each retrieve their most recent savedcheckpoint of their status and re-start the project from that checkpointto minimize lost progress on the project being executed. In someexamples, a communications grid computing system 400 can be used togenerate a visualization related to a deep neural network.

FIG. 5 is a flow chart of an example of a process for adjusting acommunications grid or a work project in a communications grid after afailure of a node according to some aspects. The process may include,for example, receiving grid status information including a projectstatus of a portion of a project being executed by a node in thecommunications grid, as described in operation 502. For example, acontrol node (e.g., a backup control node connected to a primary controlnode and a worker node on a communications grid) may receive grid statusinformation, where the grid status information includes a project statusof the primary control node or a project status of the worker node. Theproject status of the primary control node and the project status of theworker node may include a status of one or more portions of a projectbeing executed by the primary and worker nodes in the communicationsgrid. The process may also include storing the grid status information,as described in operation 504. For example, a control node (e.g., abackup control node) may store the received grid status informationlocally within the control node. Alternatively, the grid statusinformation may be sent to another device for storage where the controlnode may have access to the information.

The process may also include receiving a failure communicationcorresponding to a node in the communications grid in operation 506. Forexample, a node may receive a failure communication including anindication that the primary control node has failed, prompting a backupcontrol node to take over for the primary control node. In analternative embodiment, a node may receive a failure that a worker nodehas failed, prompting a control node to reassign the work beingperformed by the worker node. The process may also include reassigning anode or a portion of the project being executed by the failed node, asdescribed in operation 508. For example, a control node may designatethe backup control node as a new primary control node based on thefailure communication upon receiving the failure communication. If thefailed node is a worker node, a control node may identify a projectstatus of the failed worker node using the snapshot of thecommunications grid, where the project status of the failed worker nodeincludes a status of a portion of the project being executed by thefailed worker node at the failure time.

The process may also include receiving updated grid status informationbased on the reassignment, as described in operation 510, andtransmitting a set of instructions based on the updated grid statusinformation to one or more nodes in the communications grid, asdescribed in operation 512. The updated grid status information mayinclude an updated project status of the primary control node or anupdated project status of the worker node. The updated information maybe transmitted to the other nodes in the grid to update their stalestored information.

FIG. 6 is a block diagram of a portion of a communications gridcomputing system 600 including a control node and a worker nodeaccording to some aspects. Communications grid 600 computing systemincludes one control node (control node 602) and one worker node (workernode 610) for purposes of illustration, but may include more workerand/or control nodes. The control node 602 is communicatively connectedto worker node 610 via communication path 650. Therefore, control node602 may transmit information (e.g., related to the communications gridor notifications), to and receive information from worker node 610 viacommunication path 650.

Similar to in FIG. 4, communications grid computing system (or just“communications grid”) 600 includes data processing nodes (control node602 and worker node 610). Nodes 602 and 610 comprise multi-core dataprocessors. Each node 602 and 610 includes a grid-enabled softwarecomponent (GESC) 620 that executes on the data processor associated withthat node and interfaces with buffer memory 622 also associated withthat node. Each node 602 and 610 includes database management software(DBMS) 628 that executes on a database server (not shown) at controlnode 602 and on a database server (not shown) at worker node 610.

Each node also includes a data store 624. Data stores 624, similar tonetwork-attached data stores 110 in FIG. 1 and data stores 235 in FIG.2, are used to store data to be processed by the nodes in the computingenvironment. Data stores 624 may also store any intermediate or finaldata generated by the computing system after being processed, forexample in non-volatile memory. However in certain examples, theconfiguration of the grid computing environment allows its operations tobe performed such that intermediate and final data results can be storedsolely in volatile memory (e.g., RAM), without a requirement thatintermediate or final data results be stored to non-volatile types ofmemory. Storing such data in volatile memory may be useful in certainsituations, such as when the grid receives queries (e.g., ad hoc) from aclient and when responses, which are generated by processing largeamounts of data, need to be generated quickly or on-the-fly. In such asituation, the grid may be configured to retain the data within memoryso that responses can be generated at different levels of detail and sothat a client may interactively query against this information.

Each node also includes a user-defined function (UDF) 626. The UDFprovides a mechanism for the DMBS 628 to transfer data to or receivedata from the database stored in the data stores 624 that are managed bythe DBMS. For example, UDF 626 can be invoked by the DBMS to providedata to the GESC for processing. The UDF 626 may establish a socketconnection (not shown) with the GESC to transfer the data.Alternatively, the UDF 626 can transfer data to the GESC by writing datato shared memory accessible by both the UDF and the GESC.

The GESC 620 at the nodes 602 and 610 may be connected via a network,such as network 108 shown in FIG. 1. Therefore, nodes 602 and 610 cancommunicate with each other via the network using a predeterminedcommunication protocol such as, for example, the Message PassingInterface (MPI). Each GESC 620 can engage in point-to-pointcommunication with the GESC at another node or in collectivecommunication with multiple GESCs via the network. The GESC 620 at eachnode may contain identical (or nearly identical) software instructions.Each node may be capable of operating as either a control node or aworker node. The GESC at the control node 602 can communicate, over acommunication path 652, with a client device 630. More specifically,control node 602 may communicate with client application 632 hosted bythe client device 630 to receive queries and to respond to those queriesafter processing large amounts of data.

DMBS 628 may control the creation, maintenance, and use of database ordata structure (not shown) within nodes 602 or 610. The database mayorganize data stored in data stores 624. The DMBS 628 at control node602 may accept requests for data and transfer the appropriate data forthe request. With such a process, collections of data may be distributedacross multiple physical locations. In this example, each node 602 and610 stores a portion of the total data managed by the management systemin its associated data store 624.

Furthermore, the DBMS may be responsible for protecting against dataloss using replication techniques. Replication includes providing abackup copy of data stored on one node on one or more other nodes.Therefore, if one node fails, the data from the failed node can berecovered from a replicated copy residing at another node. However, asdescribed herein with respect to FIG. 4, data or status information foreach node in the communications grid may also be shared with each nodeon the grid.

FIG. 7 is a flow chart of an example of a process for executing a dataanalysis or a processing project according to some aspects. As describedwith respect to FIG. 6, the GESC at the control node may transmit datawith a client device (e.g., client device 630) to receive queries forexecuting a project and to respond to those queries after large amountsof data have been processed. The query may be transmitted to the controlnode, where the query may include a request for executing a project, asdescribed in operation 702. The query can contain instructions on thetype of data analysis to be performed in the project and whether theproject should be executed using the grid-based computing environment,as shown in operation 704.

To initiate the project, the control node may determine if the queryrequests use of the grid-based computing environment to execute theproject. If the determination is no, then the control node initiatesexecution of the project in a solo environment (e.g., at the controlnode), as described in operation 710. If the determination is yes, thecontrol node may initiate execution of the project in the grid-basedcomputing environment, as described in operation 706. In such asituation, the request may include a requested configuration of thegrid. For example, the request may include a number of control nodes anda number of worker nodes to be used in the grid when executing theproject. After the project has been completed, the control node maytransmit results of the analysis yielded by the grid, as described inoperation 708. Whether the project is executed in a solo or grid-basedenvironment, the control node provides the results of the project.

As noted with respect to FIG. 2, the computing environments describedherein may collect data (e.g., as received from network devices, such assensors, such as network devices 204-209 in FIG. 2, and client devicesor other sources) to be processed as part of a data analytics project,and data may be received in real time as part of a streaming analyticsenvironment (e.g., ESP). Data may be collected using a variety ofsources as communicated via different kinds of networks or locally, suchas on a real-time streaming basis. For example, network devices mayreceive data periodically from network device sensors as the sensorscontinuously sense, monitor and track changes in their environments.More specifically, an increasing number of distributed applicationsdevelop or produce continuously flowing data from distributed sources byapplying queries to the data before distributing the data togeographically distributed recipients. An event stream processing engine(ESPE) may continuously apply the queries to the data as it is receivedand determines which entities should receive the data. Client or otherdevices may also subscribe to the ESPE or other devices processing ESPdata so that they can receive data after processing, based on forexample the entities determined by the processing engine. For example,client devices 230 in FIG. 2 may subscribe to the ESPE in computingenvironment 214. In another example, event subscription devices 1024a-c, described further with respect to FIG. 10, may also subscribe tothe ESPE. The ESPE may determine or define how input data or eventstreams from network devices or other publishers (e.g., network devices204-209 in FIG. 2) are transformed into meaningful output data to beconsumed by subscribers, such as for example client devices 230 in FIG.2.

FIG. 8 is a block diagram including components of an Event StreamProcessing Engine (ESPE) according to some aspects. ESPE 800 may includeone or more projects 802. A project may be described as a second-levelcontainer in an engine model managed by ESPE 800 where a thread poolsize for the project may be defined by a user. Each project of the oneor more projects 802 may include one or more continuous queries 804 thatcontain data flows, which are data transformations of incoming eventstreams. The one or more continuous queries 804 may include one or moresource windows 806 and one or more derived windows 808.

The ESPE may receive streaming data over a period of time related tocertain events, such as events or other data sensed by one or morenetwork devices. The ESPE may perform operations associated withprocessing data created by the one or more devices. For example, theESPE may receive data from the one or more network devices 204-209 shownin FIG. 2. As noted, the network devices may include sensors that sensedifferent aspects of their environments, and may collect data over timebased on those sensed observations. For example, the ESPE may beimplemented within one or more of machines 220 and 240 shown in FIG. 2.The ESPE may be implemented within such a machine by an ESP application.An ESP application may embed an ESPE with its own dedicated thread poolor pools into its application space where the main application threadcan do application-specific work and the ESPE processes event streams atleast by creating an instance of a model into processing objects.

The engine container is the top-level container in a model that managesthe resources of the one or more projects 802. In an illustrativeexample, there may be only one ESPE 800 for each instance of the ESPapplication, and ESPE 800 may have a unique engine name. Additionally,the one or more projects 802 may each have unique project names, andeach query may have a unique continuous query name and begin with auniquely named source window of the one or more source windows 806. ESPE800 may or may not be persistent.

Continuous query modeling involves defining directed graphs of windowsfor event stream manipulation and transformation. A window in thecontext of event stream manipulation and transformation is a processingnode in an event stream processing model. A window in a continuous querycan perform aggregations, computations, pattern-matching, and otheroperations on data flowing through the window. A continuous query may bedescribed as a directed graph of source, relational, pattern matching,and procedural windows. The one or more source windows 806 and the oneor more derived windows 808 represent continuously executing queriesthat generate updates to a query result set as new event blocks streamthrough ESPE 800. A directed graph, for example, is a set of nodesconnected by edges, where the edges have a direction associated withthem.

An event object may be described as a packet of data accessible as acollection of fields, with at least one of the fields defined as a keyor unique identifier (ID). The event object may be created using avariety of formats including binary, alphanumeric, XML, etc. Each eventobject may include one or more fields designated as a primary identifier(ID) for the event so ESPE 800 can support operation codes (opcodes) forevents including insert, update, upsert, and delete. Upsert opcodesupdate the event if the key field already exists; otherwise, the eventis inserted. For illustration, an event object may be a packed binaryrepresentation of a set of field values and include both metadata andfield data associated with an event. The metadata may include an opcodeindicating if the event represents an insert, update, delete, or upsert,a set of flags indicating if the event is a normal, partial-update, or aretention generated event from retention policy management, and a set ofmicrosecond timestamps that can be used for latency measurements.

An event block object may be described as a grouping or package of eventobjects. An event stream may be described as a flow of event blockobjects. A continuous query of the one or more continuous queries 804transforms a source event stream made up of streaming event blockobjects published into ESPE 800 into one or more output event streamsusing the one or more source windows 806 and the one or more derivedwindows 808. A continuous query can also be thought of as data flowmodeling.

The one or more source windows 806 are at the top of the directed graphand have no windows feeding into them. Event streams are published intothe one or more source windows 806, and from there, the event streamsmay be directed to the next set of connected windows as defined by thedirected graph. The one or more derived windows 808 are all instantiatedwindows that are not source windows and that have other windowsstreaming events into them. The one or more derived windows 808 mayperform computations or transformations on the incoming event streams.The one or more derived windows 808 transform event streams based on thewindow type (that is operators such as join, filter, compute, aggregate,copy, pattern match, procedural, union, etc.) and window settings. Asevent streams are published into ESPE 800, they are continuouslyqueried, and the resulting sets of derived windows in these queries arecontinuously updated.

FIG. 9 is a flow chart of an example of a process including operationsperformed by an event stream processing engine according to someaspects. As noted, the ESPE 800 (or an associated ESP application)defines how input event streams are transformed into meaningful outputevent streams. More specifically, the ESP application may define howinput event streams from publishers (e.g., network devices providingsensed data) are transformed into meaningful output event streamsconsumed by subscribers (e.g., a data analytics project being executedby a machine or set of machines).

Within the application, a user may interact with one or more userinterface windows presented to the user in a display under control ofthe ESPE independently or through a browser application in an orderselectable by the user. For example, a user may execute an ESPapplication, which causes presentation of a first user interface window,which may include a plurality of menus and selectors such as drop downmenus, buttons, text boxes, hyperlinks, etc. associated with the ESPapplication as understood by a person of skill in the art. Variousoperations may be performed in parallel, for example, using a pluralityof threads.

At operation 900, an ESP application may define and start an ESPE,thereby instantiating an ESPE at a device, such as machine 220 and/or240. In an operation 902, the engine container is created. Forillustration, ESPE 800 may be instantiated using a function call thatspecifies the engine container as a manager for the model.

In an operation 904, the one or more continuous queries 804 areinstantiated by ESPE 800 as a model. The one or more continuous queries804 may be instantiated with a dedicated thread pool or pools thatgenerate updates as new events stream through ESPE 800. Forillustration, the one or more continuous queries 804 may be created tomodel business processing logic within ESPE 800, to predict eventswithin ESPE 800, to model a physical system within ESPE 800, to predictthe physical system state within ESPE 800, etc. For example, as noted,ESPE 800 may be used to support sensor data monitoring and management(e.g., sensing may include force, torque, load, strain, position,temperature, air pressure, fluid flow, chemical properties, resistance,electromagnetic fields, radiation, irradiance, proximity, acoustics,moisture, distance, speed, vibrations, acceleration, electricalpotential, or electrical current, etc.).

ESPE 800 may analyze and process events in motion or “event streams.”Instead of storing data and running queries against the stored data,ESPE 800 may store queries and stream data through them to allowcontinuous analysis of data as it is received. The one or more sourcewindows 806 and the one or more derived windows 808 may be created basedon the relational, pattern matching, and procedural algorithms thattransform the input event streams into the output event streams tomodel, simulate, score, test, predict, etc. based on the continuousquery model defined and application to the streamed data.

In an operation 906, a publish/subscribe (pub/sub) capability isinitialized for ESPE 800. In an illustrative embodiment, a pub/subcapability is initialized for each project of the one or more projects802. To initialize and enable pub/sub capability for ESPE 800, a portnumber may be provided. Pub/sub clients can use a host name of an ESPdevice running the ESPE and the port number to establish pub/subconnections to ESPE 800.

FIG. 10 is a block diagram of an ESP system 1000 interfacing betweenpublishing device 1022 and event subscribing devices 1024 a-c accordingto some aspects. ESP system 1000 may include ESP device or subsystem1001, publishing device 1022, an event subscribing device A 1024 a, anevent subscribing device B 1024 b, and an event subscribing device C1024 c. Input event streams are output to ESP device or subsystem 1001by publishing device 1022. In alternative embodiments, the input eventstreams may be created by a plurality of publishing devices. Theplurality of publishing devices further may publish event streams toother ESP devices. The one or more continuous queries instantiated byESPE 800 may analyze and process the input event streams to form outputevent streams output to event subscribing device A 1024 a, eventsubscribing device B 1024 b, and event subscribing device C 1024 c. ESPsystem 1000 may include a greater or a fewer number of event subscribingdevices of event subscribing devices.

Publish-subscribe is a message-oriented interaction paradigm based onindirect addressing. Processed data recipients specify their interest inreceiving information from ESPE 800 by subscribing to specific classesof events, while information sources publish events to ESPE 800 withoutdirectly addressing the receiving parties. ESPE 800 coordinates theinteractions and processes the data. In some cases, the data sourcereceives confirmation that the published information has been receivedby a data recipient.

A publish/subscribe API may be described as a library that enables anevent publisher, such as publishing device 1022, to publish eventstreams into ESPE 800 or an event subscriber, such as event subscribingdevice A 1024 a, event subscribing device B 1024 b, and eventsubscribing device C 1024 c, to subscribe to event streams from ESPE800. For illustration, one or more publish/subscribe APIs may bedefined. Using the publish/subscribe API, an event publishingapplication may publish event streams into a running event streamprocessor project source window of ESPE 800, and the event subscriptionapplication may subscribe to an event stream processor project sourcewindow of ESPE 800.

The publish/subscribe API provides cross-platform connectivity andendianness compatibility between ESP application and other networkedapplications, such as event publishing applications instantiated atpublishing device 1022, and event subscription applications instantiatedat one or more of event subscribing device A 1024 a, event subscribingdevice B 1024 b, and event subscribing device C 1024 c.

Referring back to FIG. 9, operation 906 initializes thepublish/subscribe capability of ESPE 800. In an operation 908, the oneor more projects 802 are started. The one or more started projects mayrun in the background on an ESP device. In an operation 910, an eventblock object is received from one or more computing device of thepublishing device 1022.

ESP subsystem 800 may include a publishing client 1002, ESPE 800, asubscribing client A 1004, a subscribing client B 1006, and asubscribing client C 1008. Publishing client 1002 may be started by anevent publishing application executing at publishing device 1022 usingthe publish/subscribe API. Subscribing client A 1004 may be started byan event subscription application A, executing at event subscribingdevice A 1024 a using the publish/subscribe API. Subscribing client B1006 may be started by an event subscription application B executing atevent subscribing device B 1024 b using the publish/subscribe API.Subscribing client C 1008 may be started by an event subscriptionapplication C executing at event subscribing device C 1024 c using thepublish/subscribe API.

An event block object containing one or more event objects is injectedinto a source window of the one or more source windows 806 from aninstance of an event publishing application on publishing device 1022.The event block object may be generated, for example, by the eventpublishing application and may be received by publishing client 1002. Aunique ID may be maintained as the event block object is passed betweenthe one or more source windows 806 and/or the one or more derivedwindows 808 of ESPE 800, and to subscribing client A 1004, subscribingclient B 806, and subscribing client C 808 and to event subscriptiondevice A 1024 a, event subscription device B 1024 b, and eventsubscription device C 1024 c. Publishing client 1002 may furthergenerate and include a unique embedded transaction ID in the event blockobject as the event block object is processed by a continuous query, aswell as the unique ID that publishing device 1022 assigned to the eventblock object.

In an operation 912, the event block object is processed through the oneor more continuous queries 804. In an operation 914, the processed eventblock object is output to one or more computing devices of the eventsubscribing devices 1024 a-c. For example, subscribing client A 1004,subscribing client B 1006, and subscribing client C 1008 may send thereceived event block object to event subscription device A 1024 a, eventsubscription device B 1024 b, and event subscription device C 1024 c,respectively.

ESPE 800 maintains the event block containership aspect of the receivedevent blocks from when the event block is published into a source windowand works its way through the directed graph defined by the one or morecontinuous queries 804 with the various event translations before beingoutput to subscribers. Subscribers can correlate a group of subscribedevents back to a group of published events by comparing the unique ID ofthe event block object that a publisher, such as publishing device 1022,attached to the event block object with the event block ID received bythe subscriber.

In an operation 916, a determination is made concerning whether or notprocessing is stopped. If processing is not stopped, processingcontinues in operation 910 to continue receiving the one or more eventstreams containing event block objects from the, for example, one ormore network devices. If processing is stopped, processing continues inan operation 918. In operation 918, the started projects are stopped. Inoperation 920, the ESPE is shutdown.

As noted, in some examples, big data is processed for an analyticsproject after the data is received and stored. In other examples,distributed applications process continuously flowing data in real-timefrom distributed sources by applying queries to the data beforedistributing the data to geographically distributed recipients. Asnoted, an event stream processing engine (ESPE) may continuously applythe queries to the data as it is received and determines which entitiesreceive the processed data. This allows for large amounts of data beingreceived and/or collected in a variety of environments to be processedand distributed in real time. For example, as shown with respect to FIG.2, data may be collected from network devices that may include deviceswithin the internet of things, such as devices within a home automationnetwork. However, such data may be collected from a variety of differentresources in a variety of different environments. In any such situation,embodiments of the present technology allow for real-time processing ofsuch data.

Aspects of the present disclosure provide technical solutions totechnical problems, such as computing problems that arise when an ESPdevice fails which results in a complete service interruption andpotentially significant data loss. The data loss can be catastrophicwhen the streamed data is supporting mission critical operations, suchas those in support of an ongoing manufacturing or drilling operation.An example of an ESP system achieves a rapid and seamless failover ofESPE running at the plurality of ESP devices without serviceinterruption or data loss, thus significantly improving the reliabilityof an operational system that relies on the live or real-time processingof the data streams. The event publishing systems, the event subscribingsystems, and each ESPE not executing at a failed ESP device are notaware of or effected by the failed ESP device. The ESP system mayinclude thousands of event publishing systems and event subscribingsystems. The ESP system keeps the failover logic and awareness withinthe boundaries of out-messaging network connector and out-messagingnetwork device.

In one example embodiment, a system is provided to support a failoverwhen event stream processing (ESP) event blocks. The system includes,but is not limited to, an out-messaging network device and a computingdevice. The computing device includes, but is not limited to, one ormore processors and one or more computer-readable mediums operablycoupled to the one or more processor. The processor is configured toexecute an ESP engine (ESPE). The computer-readable medium hasinstructions stored thereon that, when executed by the processor, causethe computing device to support the failover. An event block object isreceived from the ESPE that includes a unique identifier. A first statusof the computing device as active or standby is determined. When thefirst status is active, a second status of the computing device as newlyactive or not newly active is determined. Newly active is determinedwhen the computing device is switched from a standby status to an activestatus. When the second status is newly active, a last published eventblock object identifier that uniquely identifies a last published eventblock object is determined. A next event block object is selected from anon-transitory computer-readable medium accessible by the computingdevice. The next event block object has an event block object identifierthat is greater than the determined last published event block objectidentifier. The selected next event block object is published to anout-messaging network device. When the second status of the computingdevice is not newly active, the received event block object is publishedto the out-messaging network device. When the first status of thecomputing device is standby, the received event block object is storedin the non-transitory computer-readable medium.

FIG. 11 is an example of a GUI 1100 for visualizing deep neural networksaccording to some aspects. The GUI 1100 can enable a user to (1) explorea deep neural network using one or more visualizations of the deepneural network; (2) quickly determine information about the deep neuralnetwork based on color coding; (3) flexibly control and focus on desiredvisual information with threshold, inspection, and tooltip operations;(4) explore, discover, and compare patterns associated with the deepneural network; or (5) any combination of these. A user may be able toanalyze a deep neural network from new perspectives and uncover insightsinto how the deep neural network functions using the GUI 1100.

In the example shown in FIG. 11, the GUI 1100 includes a node-linkdiagram 1102 that visually represents nodes (neurons) in a deep neuralnetwork and connections (links) between the nodes. The nodes can bevisually represented using circles or any other symbol. For example, thenode-link diagram 1102 can visually represent an input layer 1104 of thedeep neural network as one row of circles and an output layer 1110 ofthe deep neural network as another row of circles. The node-link diagram1102 can visually represent hidden layers 1106, 1108 of the deep neuralnetwork as rows of circles between the input layer 1104 and the outputlayer 1110. The connections between the nodes in the deep neural networkcan be visually represented using lines or other symbols.

The node-link diagram 1102 can be color coded. In some examples, thesymbols representing the nodes, the connections, or both can be coloredcoded to indicate values associated with the nodes, the connections, orboth. For example, a circle representing a node can have a first color(e.g., blue) if a value associated with the node is negative and asecond color (e.g., red) if the value associated with the node ispositive. As another example, a line representing a connection betweentwo nodes can have the first color if a value associated with theconnection is negative and the second color if the value associated withthe connection is positive. In either example, more saturated colors canrepresent larger absolute values. A legend 1118 can be provided toindicate the meaning behind the color coding to a user. The color codingmay highlight the nodes and connections with high impact (e.g., that arehighly influential) in the deep neural network, while reducing thevisual influence of the nodes with lower impact.

In some examples, the GUI 1100 can have multiple color-coding schemes.For example, the nodes in the output layer 1110 can have a differentcolor-coding, such as a grayscale color-coding, than the nodes in therest of the layers 1104-1108. The grayscale color-coding can representprobabilities of the outputs associated with the nodes in the outputlayer 1110. Another legend 1120 can be provided to indicate the meaningbehind the color scheme for the output layer 1110. Using separatecolor-coding schemes can allow more color differentiation among nodesand connections.

In the example shown in FIG. 11, the symbols in the node-link diagram1102 are color coded to represent how the deep neural network respondedto a user input (specifically, the word “animation”). A user may be ableto provide any desired input via input box 1132. A computing device canreceive the user input, feed the user input into the deep neuralnetwork, and update the color coding of the node-link diagram 1102 basedon the results. For example, a representation of a node in the inputlayer 1104 can be color coded to indicate a weight of the node in thedeep neural network (e.g., in response to user input). A representationof a connection between a node in the input layer 1104 and another nodein the hidden layer 1106 can be color coded to indicate the result ofmultiplying a first weight of the connection (in the deep neuralnetwork) by a second weight of the node from the input layer 1104. Arepresentation of a node in a hidden layer 1106, 1108 can be color codedto indicate a value determined by summing the weights of all of theconnections to the node and passing the result through a rectifiedlinear unit function. A representation of a connection between a node inthe hidden layer 1108 and another node in the output layer 1110 can becolor coded to indicate the result of multiplying a first weight of theconnection by a second weight of the node from the hidden layer 1108. Arepresentation of a node in the output layer 1110 can be color coded toindicate a value determined by summing the weights of all theconnections coming into the node and then normalizing the result torepresent the probability. The node-link diagram 1102 can be color codedto represent any number and combination of information, which can begenerated in response to any number and combination of inputs to thedeep neural network.

In some examples, visual clutter can accumulate when all the symbolsrepresenting nodes and connections are visible in the node-link diagram1102, making it difficult to locate potentially useful information. Tohelp reduce visual clutter, the GUI 1100 can include one or morethreshold controls 1124 a-d. The threshold controls 1124 a-d can enablea user to select a number of nodes to color code for a particular layerof the deep neural network. For example, threshold control 1124 a canenable a user to input a threshold number of nodes to color code for theinput layer 1104 of the deep neural network. Threshold control 1124 bcan enable a user to input a threshold number of nodes to color code forthe hidden layer 1106 of the deep neural network. Threshold control 1124c can enable a user to input a threshold number of nodes to color codefor the hidden layer 1108 of the deep neural network. Threshold control1124 d can enable a user to input a threshold number of nodes to colorcode for the output layer 1110 of the deep neural network. In responseto manipulating the threshold controls 1124 a-d, in some examples, thethreshold number of nodes can be randomly selected from the layer andcolor coded. The remaining nodes can be colored with a default color,such as light gray, or visually hidden to reduce their visual impact. Inother examples, the nodes for the layer can be ranked by theirassociated values and then color-coded until the threshold number ofnodes for that layer is reached. For example, the nodes in the inputlayer 1104 can be ordered from highest value to lowest value. Thehighest-value nodes from the ordered list can be color coded until thethreshold number of nodes is reached. The remaining nodes can be coloredwith the default color or visually hidden. In some examples, thenode-link diagram 1102 may also be updated in response to thethresholding to only display the connections between color-coded nodes(i.e., the nodes that are not the default color or visually hidden).This may decrease the number of lines in the node-link diagram 1102,reducing visual clutter.

In some examples, the GUI 1100 can include additional threshold controls1122 a-c for enabling a user to manipulate the number of connectionsvisually displayed between layers in the node-link diagram 1102. Forexample, the threshold control 1122 a can be used to apply one or morethresholds to the connections between the input layer 1104 and thehidden layer 1106. In FIG. 11, the user has selected an upper thresholdof 0.771 and a lower threshold of −1.697 via threshold control 1122 a.In response, connections having values above the upper threshold andbelow the lower threshold can be visually displayed in the node-linkdiagram 1102. Connections having values between the upper threshold andthe lower threshold can be hidden from view. This may reduce visualclutter in the node-link diagram 1102. The threshold control 1122 b cancontrol the connections visually displayed between the hidden layers1106, 1108, and threshold control 1122 c can control the connectionsvisually displayed between the hidden layer 1108 and the output layer1110. The threshold controls 1122 a-c may serve to reduce the visualclutter in the node-link diagram 1102.

A particular example of thresholding is shown in FIGS. 12A-B. The GUI1202 of FIG. 12A includes a node-link diagram showing all of theconnections (as lines) between nodes. Limited thresholding has beenapplied. The GUI 1204 of FIG. 12B shows the node-link diagram after morethresholding has been applied. The number of connections visuallydisplayed in the node-link diagram is substantially reduced as a resultof the thresholding. This can enable a user to selectively inspectfeatures of interest in the deep neural network.

Node-link diagrams can provide an intuitive visualization of thestructure of a deep neural network. But node-link diagrams may becomelarge and confusing as the number of symbols representing nodes andconnections increases for larger-sized deep neural networks (e.g., fordeep neural networks with tens or hundreds of hidden layers and nodes).For examples, lines and circles may overlap, visually occludingimportant information. Some examples of the present disclosure canovercome this issue by additionally or alternatively including a quiltgraph in the GUI 1100.

A quilt graph can include a matrix of cells, where each cell in thematrix represents a potential connection between two nodes. Because eachconnection is represented in a separate cell in the matrix, quilt graphsmay be expandable in size without becoming overly confusing. Quiltgraphs may also visually emphasize connections between nodes andhighlight patterns in the connections. For example, referring to FIG.13, nine nodes are shown using a node-link diagram. The node-linkdiagram may provide an intuitive visualization of the structure of thenodes. The same nine nodes are represented as a quilt graph in FIG. 14.The quilt graph of FIG. 14 may more readily highlight features andpatterns associated with the connections between the nodes, but mayprovide a less intuitive visualization of the structure of the nodes.

Returning to FIG. 11, the GUI 1100 includes a quilt graph 1112representing nodes and connections in the deep neural network. The quiltgraph 1112 has a horizontal axis and a vertical axis. Symbols (e.g.,circles) representing the input layer 1104 of the deep neural networkcan be positioned adjacent to the horizontal axis. Symbols representingthe hidden layer 1106 can be positioned adjacent to the vertical axis.Each cell (or “block”) in the quilt graph 1112 can represent a potentialconnection between one node in the input layer 1104 and another node inthe hidden layer 1106. Cells that are color coded can represent actualconnections between nodes in the input layer 1104 and the hidden layer1106. In some examples, the color coding of the quilt graph 1112 is thesame as the color coding for the node-link diagram 1102. For example, acell in the quilt graph 1112 can have a first color (e.g., blue) if thevalue associated with the cell is negative and a second color (e.g.,red) if the value associated with the cell is positive. More saturatedcolors can represent larger absolute values.

In some examples, the GUI 1100 includes multiple quilt graphs 1112,1114, 1116. Quilt graph 1114 can visually represent connections betweenthe hidden layer 1106 and the hidden layer 1108. Quilt graph 1116 canvisually represent connections between the hidden layer 1108 and theoutput layer 1110. These quilt graphs 1114, 1116 can use the same colorcoding or a different color coding as the node-link diagram 1102.

In some examples, the quilt graphs 1112-1116 can correspond to thenode-link diagram 1102. For example, if the node-link diagram 1102 ismanipulated, updated, or otherwise changed, the information in the quiltgraphs 1112-1116 can correspondingly change (e.g., so that theinformation presented in the quilt graphs 1112-1116 matches theinformation in the node-link diagram 1102). As a specific example, ifthresholding is applied to the node-link diagram 1102, similarthresholding can be applied to the quilt graphs 1112-1116, so that thethresholding acts on the node-link diagram 1102 and the quilt graphs1112-1116 concurrently. Likewise, if the quilt graphs 1112-1116 aremanipulated, updated, or otherwise changed, the information in thenode-link diagram 1102 can correspondingly change (e.g., so that theinformation presented in node-link diagram 1102 matches the informationin the quilt graphs 1112-1116).

While thresholding can be effective for filtering and selecting visualinformation, some examples also provide the ability to inspect each nodeseparately. For example, a user may click on a node (e.g., in thenode-link diagram 1102) to enter an inspection mode. In the inspectionmode, the node-link diagram 1102 can be updated to only display theconnections to and from the selected node, while hiding all the otherconnections. This may enable a user to focus on a single node. The quiltgraphs 1112-1116 can be similarly updated. An example of a node 1502being inspected is shown in FIG. 15. In some examples, inspecting a nodecan highlight differences between a node's incoming and outgoingconnections by reducing visual interference. This can help a userdetermine the contribution of each incoming connection to the finaloutput of a node, as well as the influence of the node on its outgoingconnections. In some examples, thresholding can be disabled in theinspection mode. In such examples, thresholding can be re-enabled uponthe GUI 1100 exiting the inspection mode. Inspection mode can be enabledor disabled via one or more options, such as option 1140.

In some examples, the GUI 1100 can present a dialog box with insightsabout a node. This can be an insights dialog-box. The insightsdialog-box can be presented in response to selection of the node. Theinsights dialog-box can indicate which input characters andcorresponding embedded weights most strongly activate the selected node.An example of an insights dialog-box 1604 is shown in FIG. 16. In FIG.16, the insights dialog-box 1604 is presented in response to a selectionof node 1602. The insights dialog-box 1604 displays the input characters(“interesting interesting”) into the deep neural network. The inputcharacters that activate the selected node 1602 can be shown in a firstcolor (e.g., red). These characters can be referred to as activatingcharacters. In FIG. 16, the activating characters are “i”, “s”, and “t”in the left occurrence of “interesting,” and “t”, “e”, “r”, “i”, and “g”in the right occurrence of “interesting.” The remaining characters canbe shown in a second, default color (e.g., light gray). The morestrongly a character activates the node 1602, the more saturated thecolor of the character may be. In some examples, the insights dialog-box1604 can also include numbers 1606 beneath the activating charactersthat identify the specific embedded-weights that produce the activation.For example, in FIG. 16, the specific embedded input-weights that drivethe activation are in positions 0, 3, 2, 2, 2&3, 4, 2, and 1&4,respectively. The insights dialog-box 1604 may allow the user todetermine which part(s) of an input that a node responds to, providinginsight into the purpose of the node. The insights dialog-box 1604 canbe enabled or disabled via one or more options, such as option 1142 ofFIG.

In some examples, the GUI 1100 can provide tooltips to display detailedinformation about individual nodes and connections. For example,hovering over a node in the input layer 1104 can cause a tooltip to bedisplayed that includes the node's position, weight, or both of these.Hovering over a connection between a node in the input layer 1104 andanother node in the hidden layer 1106 can cause a tooltip to bedisplayed that includes (i) an activation weight for the node in theinput layer 1104, (ii) a weight for the connection, (iii) an outputvalue determined by the multiplying these two weights, or (iv) anycombination of these. Hovering over a node in the hidden layer 1106,1108 can cause a tooltip to be displayed that includes (i) a sum of theincoming-connection weights, (ii) a bias value, (iii) an output valuethat can be determined by applying a function to the sum and bias, or(iv) any combination of these. Hovering over a connection between a nodein the hidden layer 1108 and a node in the output layer 1110 can cause atooltip to be displayed that includes (i) an activation weight for thenode in the hidden layer 1108, (ii) a weight for the connection, (iii)an output value determined by multiplying these two weights, or (iv) anycombination of these. Hovering over a node in the output layer 1110 cancause a tooltip to be displayed that includes (i) a sum of all of itsincoming-connection weights, (ii) a bias value, (iii) a normalizedoutput as a probability value, or (iv) any combination of these. Anexample of a tooltip 1128 generated by hovering over a node in theoutput layer 1110 is shown in FIG. 11. Tooltips may be useful to examinethe exact values of the nodes and to understand the data flow betweennodes and connections.

In some examples, a user may be able to save some or all of theinformation displayed in the GUI 1100 by manipulating a save button1138. For example, the user can save information related to the nodesand connections displayed in the node-link diagram 1102 to a data fileby selecting the save button 1138. This may enable the GUI 1100 torevert back to a previously saved state or display previously savedinformation. For example, the GUI 1100 may be able to retrieve the datafile and display the information in the data file.

In some examples, the GUI 1100 can operate in one of several operationalmodes. A user can switch between operational modes by selecting one ofthe available tabs 1130, 1134, 1136. For example, in FIG. 11, the“Input” tab 1130 has been selected and, as a result, the GUI 1100 is inan input mode. In the input mode, a user can provide an input word tothe deep neural network via the input box 1132. Based on the input, theweights of nodes and connections in the deep neural network arecalculated (e.g., by executing the deep neural network) and the GUI 1100is appropriately updated. A user can then explore the GUI 1100 using theabovementioned functions. The input mode can support examination of thedeep neural network in response to arbitrary input. This may allow auser to obtain a general idea of how the network behaves for differentclasses of inputs.

In some examples, a user may be able to select the “Animate” tab 1134 tocause the GUI 1100 to enter an animation mode. An example of a GUI 1702in animation mode is shown in FIG. 17. In animation mode, GUI 1702 cananimate the visualization using aggregate values resulting from multipleinputs into the deep neural network. As a particular example, the deepneural network may be a part-of-speech tagger that determines a part ofspeech for an input word. The user may be able to select apart-of-speech from an input element, such as a dropdown menu 1704, andthen press a start button. This can cause a predetermined number ofwords having the selected part-of-speech to be fed into the deep neuralnetwork (e.g., at a user-chosen animation speed). For example,one-hundred words that are nouns can be fed into the deep neuralnetwork. After each word is fed into the deep neural network, the GUI1702 can be updated to reflect the average activation-values resultingfrom all of the input words thus far. For example, the color coding ofthe node-link diagram and the quilt graph(s) can be updated to reflectaverage activation-values generated based on all of the input words thusfar. In some examples, the deep neural network can converge to a stablepattern of activation for a specific part-of-speech after a certainnumber of words having that part-of-speech are fed into the deep neuralnetwork. Stable patterns of activation can occur when the node andconnection activation values remain relatively constant. Stable patternsof activation can emerge after a few inputs into the deep neuralnetwork, and persist thereafter. For example, a stable pattern ofactivation can emerge after fifteen nouns are fed into the deep neuralnetwork. An example of such a stable pattern of activation is shown inthe node-link diagrams and quilt graphs of FIG. 17. The stable patternof activation may be observable by and valuable for a user.

In some examples, the stable patterns of activation can indicate that,for inputs of a common type (e.g., words that are the samepart-of-speech; words that have the same ending, such as -ly; imagescontaining a common object; or text having a common sentiment), thereare a shared set of nodes and connections that represent a signature inthe deep neural network. The deep neural network has learned, throughtraining, to use a certain pattern to process input of a given type, andwe can see that pattern in the GUI 1702. The pattern can revealimportant nodes and connections that activate most strongly for a giventype of input. This can be used to further study the features,distributions, and correlations of the nodes and connections in the deepneural network. The pattern can also visually expose some of theunderlying features of the deep neural network.

In some examples, a user may be able to select the “Compare” tab 1136 tocause the GUI 1100 to enter a comparison mode. An example of a GUI 1802in comparison mode is shown in FIG. 18. In the comparison mode, a usermay be able to select two files via file-selection components 1804 a-b.The files may each have saved information associated with nodes andconnections between the nodes in a deep neural network. For example, thefile “gaga.json” selected using file-selection component 1804 a caninclude information indicating how the deep neural network respondedwhen the word “gaga” was input into the deep neural network. The file“Gaga.json” selected using file-selection component 1804 b can includeinformation indicating how the deep neural network responded when theword “Gaga” (with a capital G) was input into the deep neural network.In some examples, a user can select a button associated with one of thefiles to cause the GUI 1802 to display the information in the file. Forexample, the user can select the “vis” button adjacent to file-selectioncomponent 1804 a to cause the GUI 1802 to display the information in thefile “gaga.json.” In other examples, the user can select a “compare”button to cause the GUI 1802 to determine and display differencesbetween the information in the two files.

For example, the activation pattern (e.g., the activation values forevery node and connection) in “gaga.json” can be compared to theactivation pattern in “Gaga.json,” and differences between the twoactivation-patterns can be calculated and displayed. The node-linkdiagram and the quilt graphs can be color coded to represent thedifferences between the two activation-patterns. This may enable a userto visualize how activation differences among nodes in the input layerpropagate to nodes in the hidden layers and the output layer. Forexample, in the quilt graph, the columns representing the firstcharacter “G” (versus “g”) in the input are most different in theiractivation values. This can indicate that the difference in the firstcharacter is responsible for the majority of the difference in the deepneural network's response to “Gaga” versus “gaga.” In the node-linkdiagram, the outgoing connections from the nodes associated with theletter “G” are linked to every node in the first hidden layer. Thesedifferences further propagate to the output layer, affecting thedifferent probabilities used to classify both inputs. This canintuitively illustrate that a difference concentrated in a small area inthe input layer propagates and disseminates to a broader area in thefollowing layers, influencing the behavior of the entire deep neuralnetwork.

In some examples, comparing information in multiple files may allow auser to determine how a deep neural network responds to differentinputs, identify nodes and connections that activate most differently inresponse to different inputs, or compare an average activation-patternresulting from multiple inputs (e.g., generated using the animationmode) to an activation pattern resulting from a single input. Further,local changes in the deep neural network have global consequences, whichcan be visualized via the GUI 1802. Applying the same analysis to otherdeep neural networks can reveal how local changes influence those deepneural networks.

In some examples, thresholding can be applied and nodes can be selected,for example, as shown in FIG. 18B. This can allow a user to inspectnodes to see which differences in the input layer are contributing mostto the activation of the selected node.

Scalability can be an important consideration for a GUI for visualizingdeep neural networks. For example, the deep neural network depicted inthe GUI 1100 of FIG. 11 includes four layers (an input layer 1104,hidden layers 1106-1108, and an output layer 1110). But other deepneural networks can include tens or hundreds of layers having hundredsor thousands of nodes each. Some examples can include features to enablethe GUI 1100 to scale for different-sized deep neural networks.

For example, the GUI 1100 can provide the ability to interactively“activate” or “deactivate” individual layers or groups of layers. Aspecific example is shown in FIG. 19. In FIG. 19, layers 1902, 1904, and1908 are “activated” such that the nodes and connections between thenodes in the layers 1902, 1904, and 1908 are visible. Layers 1906 a-care “deactivated” such that some or all of the connections between thenodes are hidden. These layers can be referred to as deactivated layers.But the nodes in the deactivated layers 1906 a-c may still be visibleand color coded to enable a user to visually determine information aboutthe nodes quickly and easily. This can be referred to as layersummarization. This can allow a user to examine the activation valuesfor the nodes in deactivated layers 1906 a-c. For example, eachdeactivated layer 1906 a-c can be divided into n sub-blocks, with onesub-block representing one node. Each sub-block is colored based on theactivation value of the node it represents. In some examples, the orderof the sub-blocks can be sorted based on the activation values of thenodes they represent. This may allow a user to better compare theactivation patterns between adjacent, deactivated layers 1906 a-c.Further, layers 1906 a-c that have similar color-patterns for sub-blocksmay be candidates for further examination, since they may identifyhidden layers that perform similar functionality, and therefore may becompressed to reduce the size of the deep neural network.

In some examples, the connections between adjacent layers L1 and L2 caneither be shown or hidden according to the following rules. If both L1and L2 are activated, the connections between L1 and L2 can bedisplayed. If both L1 and L2 are deactivated, the connections between L1and L2 can be hidden. If L1 is activated and L2 is deactivated, for eachnode in L1, a single connection can be displayed that represents theaverage weight of all the connections from the node in L1 to the nodesin L2.

In some examples, the nodes in the node-link diagram 1102 can becompressed or merged to reduce the amount of information displayed inthe node-link diagram. These can be referred to as node summarization.An example of node compression and node merging is shown in FIG. 20.FIG. 20 shows a row of nodes 2002. The row of nodes 2002 may represent,for example, a hidden layer 1106 of nodes in the in the node-linkdiagram 1102 of FIG. 11. The left-hand side of FIG. 20 shows an exampleof node merging. In node merging, nodes having the same activation-valuecan be merged together, thereby reducing the overall number of nodes.This can result in merged nodes 2004. In some such examples, prior tomerging the nodes together, a threshold can be applied to the nodes suchthat only nodes having a high activation-value or a low activation-valueare retained and used during the merging process. In other suchexamples, prior to merging the nodes together, nodes exhibitinghigh-variance patterns (e.g., positive, negative, positive, negative)can be reordered to remove the variance (e.g., positive, positive,negative, negative). The right-hand side of FIG. 20 shows an example ofnode compression. In node compression, nodes having the same sign (e.g.,a positive activation-value or a negative activation-value) can bemerged together into a single node, thereby reducing the overall numberof nodes. This can result in compressed nodes 2006.

In some examples, changes to a node-link diagram 1102 affect the quiltgraph(s) 1112-1116 in a corresponding manner. For example, deactivatingone or both of the hidden layers 1106, 1108 can reduce the matrix of thequilt graph 1112 to a 1×n vector (or n×1 vector, depending onorientation). And merging or compressing n nodes into m representativenodes, wherein m≦n, can reduce the width (or height) of thecorresponding quilt graph(s) 1112-1116 from n to m. One example of aquilt graph 2100 resulting from the layer deactivations shown in FIG. 19is shown in FIG. 21.

Some or all of the abovementioned features may enable the GUI 1100 toscale for use with deep neural networks having varying numbers of layersand nodes. The GUI 1100 can work with any type of deep neural network,including but not limited to deep neural networks for determiningparts-of-speech, image classification, and natural language processing.The GUI 1100 can work with convolutional neural networks, recurrentneural networks, etc.

FIG. 22 is a flow chart of an example of a process for visualizing adeep neural network according to some aspects. Some examples can includemore steps than, fewer steps than, different steps than, or a differentorder of the steps shown in FIG. 22. Some examples can be implementedusing any of the systems and processes described with respect to FIGS.1-10.

In block 2202, a processing device receives a first plurality of valuesfor a first layer of nodes in a neural network, a second plurality ofvalues for a second layer of nodes in the neural network, a thirdplurality of values for connections between the first layer of nodes andthe second layer of nodes, or any combination of these. For example, theprocessing device can receive a first set of activation valuesassociated with nodes in an input layer of a neural network, a secondset of activation values associated with nodes in a hidden layer of theneural network, and a third set of activation values associated withconnections between the nodes in the input layer and the hidden layer.

In some examples, the processing device can receive the first pluralityof values, second plurality of values, and third plurality of valuesfrom local memory or from a remote device. For example, the processingdevice can access a local memory or a remote database (e.g., via theInternet) to obtain the first plurality of values, second plurality ofvalues, and third plurality of values. In other examples, the processingdevice can generate the first plurality of values, second plurality ofvalues, and third plurality of values by running the neural network. Theprocessing device can provide input to the neural network, train theneural network, or otherwise execute the neural network and, thereafter,determine the activation values associated with the nodes andconnections in the neural network. The processing device can use theactivation values as the first plurality of values, second plurality ofvalues, and third plurality of values.

In some examples, the processing device can perform some or all of thesteps shown in FIG. 23 to determine the first plurality of values,second plurality of values, and third plurality of values. For example,in block 2302, the processing device can execute the neural networkmultiple times using multiple different inputs. This may be similar tothe animation mode or comparison mode discussed above. In block 2304,the processing device determines the first plurality of values based onmultiple sets of values for the first layer of nodes generated byexecuting the neural network multiple times. For example, the processingdevice can determine the first plurality of values by calculating thedifference between (e.g., if in comparison mode) or average of (e.g., ifin animation mode) the multiple sets of values for the first layer ofnodes. In block 2306, the processing device determines the secondplurality of values based on multiple sets of values for the secondlayer of nodes. For example, the processing device can determine thesecond plurality of values by calculating the difference between oraverage of the multiple sets of values for the second layer of nodes. Inblock 2308, the processing device determines the third plurality ofvalues based on multiple sets of values for the connections between thefirst layer of nodes and the second layer of nodes. For example, theprocessing device can determine the third plurality of values bycalculating the difference between or average of the multiple sets ofvalues for the connections between the first layer of nodes and thesecond layer of nodes.

Returning to FIG. 22, in block 2204, the processing device causes adisplay device to output a quilt graph based on the first plurality ofvalues, second plurality of values, third plurality of values, or anycombination of these. For example, the processing device can generatethe quilt graph 1112 shown in FIG. 11 to represent at least a portion ofthe first plurality of values, second plurality of values, and thirdplurality of values.

In block 2206, the processing device causes the display device to outputa node-link diagram based on the first plurality of values, secondplurality of values, third plurality of values, or any combination ofthese. For example, the processing device can generate the node-linkdiagram 1102 shown in FIG. 11 to represent at least a portion of thefirst plurality of values, second plurality of values, and thirdplurality of values.

In block 2208, the processing device determines a fourth plurality ofvalues for a third layer of nodes in the neural network. For example,the processing device can determine a third set of activation valuesassociated with another hidden layer in the neural network or an outputlayer of the neural network. The processing device can receive ordetermine the third set of activation values using any of the methodsdescribed with respect to block 2202.

In block 2210, the processing device causes the display device to outputanother quilt graph 1114-1116 based on the fourth plurality of values.For example, the processing device can generate quilt graph 1114, quiltgraph 1116, or both (shown in FIG. 11) to represent at least a portionof the fourth plurality of values.

In some examples, the processing device can update the node-linkdiagram, the quilt graph(s), or any combination of these based on userinput. For example, referring now to FIG. 24, in block 2402 theprocessing device receives a user input indicating a selection of (avisual representation of) a particular node in the node-link diagram orthe quilt graph. The processing device can receive the user input via amouse, keyboard, touch-screen display, touch-pad, etc. In block 2404,the processing device updates a node-link diagram, one or more quiltgraph(s), or any combination of these to only display informationcorresponding to the particular node in response to the user input. Forexample, the processing device can update the node-link diagram 1102 andthe quilt graph(s) 1112-1116 to only display connections to and from theparticular node. An example of this is shown in FIG. 18B. Additionallyor alternatively, the processing device can update the node-link diagramto display a dialog box with information related to the particular node(e.g., as shown in FIG. 18B).

FIG. 25 is an example of a GUI for visualizing a convolutional neuralnetwork according to some aspects. The GUI can incorporate some or allof the features discussed above. In the example shown in FIG. 25, theGUI includes a visual representation of an input word 2502 to theconvolutional neural network. Adjacent to the visual representation ofthe input word 2502 is a matrix of cells 2504 that visually representsan input layer of the convolutional neural network. Each cell in thematrix of cells 2504 can visually represent a node in the input layer.And the matrix of cells 2504 can be color coded to represent theembeddings of the input word 2502. The convolutional neural network canlearn the embeddings during a training.

Adjacent to the matrix of cells 2504 can be another matrix of cells 2506that visually represents a convolutional layer of the convolutionalneural network. Each column in the matrix of cells 2506 can indicateactivation values (also known as “feature-map values”) in a feature mapcorresponding to a particular filter (implemented by the convolutionallayer). Each cell can correspond to a node in the convolutional layer.The cell can be color coded to represent an activation value in thefeature map corresponding to the node. In some examples, the activationvalue for a node can be determined by applying a rectified linear unitto the sum of the node's convolution matrix entries plus a biasassociated with the node.

The GUI can also include a node-link diagram 2508. The node-link diagram2508 can visually represent a pooling layer (e.g., in 2510 of theconvolutional neural network, a hidden layer 2512 of the convolutionalneural network, an output layer 2514 of the convolutional neuralnetwork, or any combination of these. An example of the pooling layer2510 can be a maxpooling layer that determines the maximum activationvalue in each column in the matrix of cells 2506. The pooling layer2510, hidden layer 2512, and output layer 2514 may collectively form afully-connected region of the convolutional neural network. In someexamples, an activation value for connection that is visuallyrepresented in the node-link diagram 2508 can be the product of itssource node's weight and the connection's weight. An activation valuefor a node in the hidden layer 2512 can be the sum of all incomingconnection activations, passed through a rectified linear unit. Anactivation value for a node in the output layer 2514 can be the sum ofall incoming connection activations, normalized to represent theprobability of the given output. The node-link diagram 2508 can be colorcoded or have any of the other features as discussed above. For example,each node in the node-link diagram 2508 can be color coded to expressits activation value.

A user can interact with the GUI to obtain additional informationrelated to the convolutional neural network. For example, referring toFIG. 26, a user may select a cell in the matrix of cells 2506 and theGUI can responsively display a tooltip 2602 that can include (i) a sizeof the filter related to the cell, (ii) a number of the filter relatedto the cell, (iii) a position in the input value to which the cellrelates, (iv) a bias value; (v) a pre-activation value; (vi) apost-activation value; or (vii) any combination of these. Additionallyor alternatively, a user can select a cell and the GUI can responsivelydisplay an insights dialog-box, which can be similar to insightsdialog-box 1604. The insights dialog-box can display the inputcharacters and corresponding embedding weights that most stronglyactivate a target node. For example, if a user selects a node in thehidden layer 2512, the GUI can display an insights dialog-box indicatingwhich input characters and embedding weights most strongly activate thatnode. In some examples, the input characters and embedding weights thatmost strongly activate a node in the hidden layer 2512 can be determinedby first assigning an input character to each node in the pooling layer2510. This can be achieved by, for each node in the pooling layer 2510,(i) determining the most highly activated node in the column of cells(in the matrix of cells 2506) above the node, and (b) assigning, to thenode in the pooling layer 2510, the input character that is centeredover the filter for the most highly activated node. After each node inthe pooling layer 2510 is assigned an input character, if the userselects a node in the hidden layer 2512 or the output layer 2514, theGUI can identify highly activated connections between the selected nodeand nodes in the pooling layer 2510. The GUI can then display aninsights dialog-box that indicates the input characters associated withthe nodes in the pooling layer 2510.

The user may also be able to hover (e.g., a mouse) over the cell and theGUI can responsively display additional matrices 2604. One matrix (e.g.,the top matrix) of the additional matrices 2604 can visually representthe weights of the filter. Another matrix (e.g., the bottom matrix) ofthe additional matrices 2604 can visually represent the convolutionmatrix of filter weights multiplied by the embedded weights for thecurrent node. In some examples, the user can select a button 2606 todisplay a quilt graph 2608 that visually represents the edges betweentwo layers in the node-link diagram 2508.

In some examples, the convolutional neural network can be relativelysmall. For example, the convolutional neural network can include 75filters, one maxpooling layer, and two fully connected layers (e.g.,with 100 nodes each). In other examples, the convolutional neuralnetwork can be relatively large. Some examples of the present disclosurecan provide tools to accommodate convolutional neural networks ofvarying sizes.

For example, the GUI can enable columns in the matrix of cells 2506 tobe clustered together, which can be referred to as horizontalclustering. In horizontal cluster, a user may provide a number of groupsinto which the columns in the matrix of cells 2506 are to be clustered.The GUI can then use k-means clustering or another clustering techniqueto group the columns in the matrix of cells 2506 into clusters. This canresult in similar feature maps being grouped together. After groupingseveral columns into a cluster, the GUI can visually represent thecluster as a single column. FIG. 27 shows an example of columns in thematrix of cells 2506 grouped into three clusters 2702 a-c. Each column2702 a-c visually represents a cluster formed from multiple columns inthe matrix of cells 2506. The cells in a column, such as column 2702 a,can be color coded to indicate the centroids of the cluster. Theclustered view can provide a summary of the feature maps and reduce thesize of the visualization in the horizontal direction. A user can selectan expand icon for a cluster (e.g., the plus icon above a column) tovisually expand the cluster, which can enable the user to see whichfeature maps belong to the cluster. An example of two expanded clustersis shown in FIG. 28. The user may select a compress icon for a cluster(e.g., the minus icon above clusters 2702 b-c) to visually re-compressthe cluster back into a single column. In some examples, a user canselect an uncluster button 2610 and the GUI can responsively return tothe original view, in which columns in the matrix of cells 2506 are notgrouped into clusters.

Additionally or alternatively, the GUI can enable adjacent rows in thematrix of cells 2504 to be merged together, which can be referred to asvertical compression. In some examples, the GUI can merge adjacent rowsin the matrix of cells 2504, as long as a Euclidian distance between theadjacent rows is below a threshold (e.g., which can be provided by auser). FIG. 29 shows an example of rows in the matrix of cells 2506merged into three groups 2902 a-c. Each row 2902 a-c visually representsa group formed from multiple rows in the matrix of cells 2506. The cellsin a row, such as row 2902 a, can be color coded to indicate the averagevalues of the merged cells. In some examples, a user can input acompression threshold that controls the number of rows that are mergedtogether. In some examples, the compressed view can provide reduce thesize of the visualization in the vertical direction. A user can selectan expand icon for a group of rows (e.g., the plus icon adjacent to arow) to visually expand the group of rows, which can enable the user tosee the rows belong to the group. An example of an expanded group isshown in FIG. 30. The user may select a compress icon for a group ofrows (e.g., the minus icon adjacent to row 2902 b) to visuallyre-compress the group of rows back into a single row. In some examples,a user can select an uncompress button 2612 and the GUI can responsivelyreturn to the original view, in which rows in the matrix of cells 2506are not merged into groups.

Horizontal clustering and vertical compression can be used together tosummarize in both the horizontal and vertical directions. For example,horizontal clustering can be applied to the matrix of cells 2506, andthen vertical compression can be applied to the matrix of cells 2506. Orvice-versa. In examples that include more than one convolutional layer(e.g., more than one matrix of cells visually representing more than oneconvolutional layer), horizontal clustering and vertical compression canbe independently applied to each convolutional layer.

Additionally or alternatively, the GUI can provide for nodesummarization. With node summarization, adjacent nodes in a layer of thenode-link diagram 2508 can be merged together into a single node, whichcan be referred to as a summarized node. In some examples, adjacentnodes may be merged together if the adjacent nodes have activationvalues that are all below or all above the average activation value ofall the nodes in the layer. In other examples, the adjacent node can bemerged together if the adjacent nodes have other similar characteristics(e.g., that are within a certain value-range from one another). Asummarized node can be color coded to indicate the average aggregationof the original nodes' activation values. Incoming and outgoingconnections for a summarized node can be color coded to indicate theaverage aggregations of the original nodes' connections. An example ofnode summarization for the pooling layer 2510, hidden layer 2512, andoutput layer 2514 of the convolutional neural network is shown in FIG.31.

The GUI can also provide for layer summarization. With layersummarization, a user can activate or deactivate layers or groups oflayers in the node-link diagram. Activated layers can retain full detail(e.g., all nodes will be visualized, and connections between adjacentlayers can be shown). Deactivated layers can be reduced to a singleline. The single line can be divided into n sub-blocks, with onesub-block representing one node. Each sub-block can be colored based onthe activation value of the node it represents. The order of thesub-blocks may or may not be sorted based on the activation values ofthe nodes they represent. In some examples, all of the connections goinginto a node in a deactivated layer can be represented as a single line.The single line can be color coded, for example, to represent an averagevalue of all of those incoming connections. Similarly, all of theconnections going out from a node in a deactivated layer can berepresented as a single line, which can be color coded (e.g., torepresent an average value of all of those outgoing connections). Anexample of layer summarization for the pooling layer 2510, hidden layer2512, and output layer 2514 of the convolutional neural network is shownin FIG. 32.

In some examples, the type of a deep neural network to be visualized canbe detected and the GUI can be adjusted accordingly. For example, theGUI can detect that a deep neural network to be visualized is a feedforward neural network and include the quilt graph 1112 of FIG. 11. Asanother example, the GUI can detect that a deep neural network to bevisualized is a convolutional neural network and include the matrix ofcells 2506 of FIG. 25.

FIG. 33 is a flow chart of an example of a process for visualizing adeep neural network according to some aspects. Some examples can includemore steps than, fewer steps than, different steps than, or a differentorder of the steps shown in FIG. 33. Some examples can be implementedusing any of the systems and processes described with respect to FIGS.1-10.

In block 3302, a processing device executes a convolutional neuralnetwork. In some examples, the processing device may train theconvolutional neural network prior to executing the convolutional neuralnetwork.

The convolutional neural network can include one or more filters foridentifying features in an input. The filters can be convolved with theinput to create feature maps having feature-map values. An example of aprocess for creating a feature-map value is shown in FIG. 34. In block3402 of FIG. 34, the convolutional neural network can convolve filtervalues from a filter with input values to generate a convolutionalmatrix. The processing device can then sum the values in theconvolutional matrix to determine a sum, as shown in block 3404.Thereafter, the convolutional neural network can apply a weight to thesum to determine a weighted sum, as shown in block 3406. Theconvolutional neural network may then use the weighted sum as afeature-map value, as shown in block 3408.

Returning to FIG. 33, in block 3304, the processing device generates amatrix of symbols (e.g., matrix of cells 2506 in FIG. 25) indicatingfeature-map values generated by the convolutional neural network. Thesymbols can form cells of the matrix and have any suitable shape, suchas circles, squares, rectangles, triangles, or any combination of these.The symbols can be color coded to indicate the feature-map values.

In block 3306, the processing device generates a node-link diagram(e.g., node-link diagram 2508 of FIG. 25) that represents layers of afeed forward neural network (or a fully connected neural network), whichcan form part of the convolutional neural network. For example, thenode-link diagram can visually represent a fully connected neuralnetwork that is positioned within the convolutional neural network afterone or more convolutional layers in the convolutional neural network.

In block 3308, the processing device generates a graphical userinterface that includes the matrix of symbols and the node-link diagram.The processing device can generate the graphical user interface at leastin part by arranging graphical elements to form the graphical userinterface. For example, the processing device can generate the graphicaluser interface by arranging the matrix of symbols above, below, oradjacent to the node-link diagram.

In block 3310, the processing device transmits a display communicationto a display device (e.g., a television, computer monitor, touch-screendisplay, liquid crystal display, or any combination of these). Thedisplay communication can be an electrical signal configured to causethe display device to output the graphical user interface.

FIG. 35 is a flow chart of an example of a process for interacting witha visualization of a deep neural network according to some aspects. Someexamples can include more steps than, fewer steps than, different stepsthan, or a different order of the steps shown in FIG. 35. Some examplescan be implemented using any of the systems and processes described withrespect to FIGS. 1-10.

In block 3502, a processing device detects an interaction with thegraphical user interface. The interaction can indicate that rows in amatrix (e.g., matrix of cells 2506) within the graphical user interfaceare to be merged together, columns in the matrix are to be groupedtogether, or both. Examples of the interaction can include selecting orhovering over a merge button or a grouping button. In some examples, theprocessing device can detect the interaction based on input signals froma user input device. For example, the processing device can receive theinput signals from a mouse, keyboard, touch-screen, joystick, or anycombination of these. The input signals can indicate that the rows inthe matrix are to be merged together, columns in the matrix are to begrouped together, or both. The processing device can receive the inputsignals and detect the interaction based on the input signals.

In block 3504, the processing device causes a display device to displayan updated version of the matrix in which the rows are merged togetherinto a single row, the columns are grouped together into a singlecolumn, or both. The processing device can cause the display device todisplay the updated version of the matrix based on detecting theinteraction in block 3504. An example of merged rows is shown in FIG. 29and an example of grouped columns is shown in FIG. 27.

In block 3506, the processing device detects another interactionindicating the single row or the single column is to be expanded.Examples of the interaction can include a selecting or hovering over anexpansion icon. The processing device can detect the interaction basedon input signals from a user input device.

In block 3508, the processing device causes the display device todisplay an updated version of the matrix in which the single row or thesingle column is expanded. The processing device can cause the displaydevice to display the updated version of the matrix based on detectingthe interaction in block 3506. An example of an expanded row is shown inFIG. 30 and an example of expanded columns is shown in FIG. 28.

In block 3510, the processing device detects another interaction with asymbol in the matrix (e.g., a cell in the matrix of cells 2506).Examples of the interaction can include a selecting or hovering over thesymbol. The processing device can detect the interaction based on inputsignals from a user input device.

In block 3512, the processing device modifies the graphical userinterface to show one or more additional matrices (e.g., additionalmatrices 2604 of FIG. 26). The processing device can modify thegraphical user interface based on detecting the interaction in block3510.

FIG. 36 is a flow chart of another example of a process for interactingwith a visualization of a deep neural network according to some aspects.Some examples can include more steps than, fewer steps than, differentsteps than, or a different order of the steps shown in FIG. 36. Someexamples can be implemented using any of the systems and processesdescribed with respect to FIGS. 1-10.

In block 3602, a processing device detects an interaction indicatingthat a layer of a feed forward neural network (e.g., a fully connectedneural network) is to be deactivated in a graphical user interface.Examples of the interaction can include selecting or hovering over adeactivation button in the graphical user interface. Examples of thelayer can include an input layer, hidden layer, or output layer of thefeed forward neural network.

In block 3604, the processing device causes a display device to visuallyrepresent the layer in the feed forward neural network as a line ofblocks. An example of this is shown in FIG. 32. The line of blocks canbe formed from sub-blocks that are color coded to indicate valuesassociated with the nodes represented by the sub-blocks.

In block 3606, the processing device causes the display device tovisually hide at least a portion of the lines in the node-link diagramthat represent connections between the layer and an adjacent layer ofthe feed forward neural network. For example, the processing device cancause the display device to visually hide a majority of the connectionsbetween a pooling layer 2510 and an adjacent hidden layer 2512. This mayreduce visual clutter in the node-link diagram.

The foregoing description of certain examples, including illustratedexamples, has been presented only for the purpose of illustration anddescription and is not intended to be exhaustive or to limit thedisclosure to the precise forms disclosed. Numerous modifications,adaptations, and uses thereof will be apparent to those skilled in theart without departing from the scope of the disclosure.

1. A system for providing an interactive visualization of aconvolutional neural network, the system comprising: a processingdevice; and a memory device on which instructions executable by theprocessing device are stored for causing the processing device to:display, via a display device, a graphical user interface comprising amatrix having rows and columns of symbols indicating feature-map valuesthat represent likelihoods of particular features being present orabsent at various locations in an input to a convolutional neuralnetwork, each column in the matrix having feature-map values generatedby convolving the input to the convolutional neural network with arespective filter for identifying a particular feature in the input;detect, via an input device, an interaction with the graphical userinterface indicating that the columns in the matrix are to be combinedinto a particular number of groups; and in response to detecting theinteraction: cluster the columns into the particular number of groupsusing a clustering method; for each group of columns, determine aplurality of average feature-map values, each average feature-map valuebeing determined by averaging the feature-map values represented in arespective row of the columns; and display an updated version of thematrix within the graphical user interface by visually representing eachrespective group of columns as a single column of symbols within thematrix, each symbol in the single column of symbols having visualcharacteristics representing an average feature-map value, from amongthe plurality of average feature-map values, that corresponds to a rowin which the symbol is positioned.
 2. The system of claim 1, wherein theinteraction is a first interaction, and wherein the memory devicefurther includes instructions executable by the processing device forcausing the processing device to: detect a second interaction with thegraphical user interface, the second interaction indicating that a groupof columns represented by a single column in the updated version of thematrix is to be expanded; and based on detecting the second interaction,display an expanded version of the matrix that visually includes all ofthe columns in the group of columns.
 3. The system of claim 2, whereinthe memory device further includes instructions executable by theprocessing device for causing the processing device to: detect a thirdinteraction indicating that the expanded version of the matrix is to becontracted; and based on detecting the third interaction, display acontracted version of the matrix in which the group of columns aremerged into the single column.
 4. The system of claim 1, wherein theinteraction is a first interaction, and wherein the memory devicefurther includes instructions executable by the processing device forcausing the processing device to: detect a second interaction with thegraphical user interface, the second interaction comprising hovering acursor over a symbol in the matrix; and based on detecting the secondinteraction, modify the graphical user interface by: displaying a firstmatrix of blocks within the graphical user interface, the first matrixof blocks being color coded to represent weights of a filter used togenerate a feature-map value indicated by the symbol; and displaying asecond matrix of blocks within the graphical user interface, the secondmatrix of blocks being color coded to represent values in aconvolutional matrix formed by multiplying the weights of the filter byembedding weights corresponding to the symbol.
 5. The system of claim 4,wherein: the clustering method includes a k-means clustering method; thefirst matrix of blocks is positioned above and adjacent to the secondmatrix of blocks; the first matrix of blocks and the second matrix ofblocks are positioned to a right of the matrix that has the rows andcolumns of symbols; and the symbols in the matrix are color coded torepresent the feature-map values.
 6. The system of claim 1, wherein theinteraction is a first interaction, and wherein the memory devicefurther includes instructions executable by the processing device forcausing the processing device to: detect a second interaction with thegraphical user interface, the second interaction indicating a thresholdvalue for compressing the rows in the matrix; and based on detecting thesecond interaction, display a compressed version of the matrix by:determining that adjacent rows in the matrix have a Euclidian distancethat is below the threshold value; and merging the adjacent rows into asingle row in the matrix, wherein the single row has symbols with visualcharacteristics that represent averages of the feature-map valuesrepresented by the adjacent rows.
 7. The system of claim 6, wherein thememory device further includes instructions executable by the processingdevice for causing the processing device to: detect a third interactionwith the graphical user interface, the third interaction indicating thata group of rows represented by a single row in the compressed version ofthe matrix is to be expanded; and based on detecting the thirdinteraction, display an expanded version of the matrix by visuallydisplaying all of the rows in the group of rows.
 8. The system of claim1, wherein the interaction is a first interaction, and wherein thememory device further includes instructions executable by the processingdevice for causing the processing device to include a node-link diagramwithin the graphical user interface, wherein the node-link diagramincludes: a first row of symbols representing an input layer to a feedforward neural network that is part of the convolutional neural network,the first row of symbols having visual characteristics representative ofvalues at the input layer; one or more rows of symbols representing oneor more hidden layers of the feed forward neural network, the one ormore rows of symbols having visual characteristics representative ofvalues at the one or more hidden layers; a final row of symbolsrepresenting an output layer of the feed forward neural network, thefinal row of symbols having visual characteristics representative ofvalues at the output layer; and lines between (i) the first row ofsymbols representing the input layer, (ii) the one or more rows ofsymbols representing the one or more hidden layers, and (ii) the finalrow of symbols representing the output layer, wherein the linesrepresent connections between the input layer, the one or more hiddenlayers, and the output layer.
 9. The system of claim 8, wherein theinteraction is a first interaction, and wherein the memory devicefurther includes instructions executable by the processing device forcausing the processing device to: detect a second interaction indicatingthat a layer of the feed forward neural network is to be deactivated inthe graphical user interface; and based on detecting the secondinteraction: visually represent the layer of the feed forward neuralnetwork as a line of blocks, each block representing a node in the layerof the feed forward neural network and being color coded to represent anactivation value of the node; and visually hide at least a portion ofthe lines that represent connections between the layer of the feedforward neural network and an adjacent layer of the feed forward neuralnetwork.
 10. The system of claim 9, wherein the memory device furtherincludes instructions executable by the processing device for causingthe processing device to: determine that the adjacent layer is also tobe deactivated in the graphical user interface; and based on determiningthat the adjacent layer is also to be deactivated in the graphical userinterface, visually hide all of the lines that represent connectionsbetween the layer and an adjacent layer.
 11. A method for providing aninteractive visualization of a convolutional neural network, the methodcomprising: displaying, by a processing device and via a display device,a graphical user interface comprising a matrix having rows and columnsof symbols indicating feature-map values that represent likelihoods ofparticular features being present or absent at various locations in aninput to a convolutional neural network, each column in the matrixhaving feature-map values generated by convolving the input to theconvolutional neural network with a respective filter for identifying aparticular feature in the input; detecting, by the processing device andvia an input device, an interaction with the graphical user interfaceindicating that the columns in the matrix are to be combined into aparticular number of groups; and in response to detecting theinteraction: clustering, by the processing device, the columns into theparticular number of groups using a clustering method; for each group ofcolumns, determining, by the processing device, a plurality of averagefeature-map values, each average feature-map value being determined byaveraging the feature-map values represented in a respective row of thecolumns; and displaying, by the processing device and via the displaydevice, an updated version of the matrix within the graphical userinterface by visually representing each respective group of columns as asingle column of symbols within the matrix, each symbol in the singlecolumn of symbols having visual characteristics representing an averagefeature-map value, from among the plurality of average feature-mapvalues, that corresponds to a row in which the symbol is positioned. 12.The method of claim 11, wherein the interaction is a first interaction,and further comprising: detecting a second interaction with thegraphical user interface, the second interaction indicating that a groupof columns represented by a single column in the updated version of thematrix is to be expanded; and based on detecting the second interaction,displaying an expanded version of the matrix that visually includes allof the columns in the group of columns.
 13. The method claim 12, furthercomprising: detecting a third interaction indicating that the expandedversion of the matrix is to be contracted; and based on detecting thethird interaction, displaying a contracted version of the matrix inwhich the group of columns are merged into the single column.
 14. Themethod of claim 11, wherein the interaction is a first interaction, andfurther comprising: detecting a second interaction with the graphicaluser interface, the second interaction comprising hovering a cursor overa symbol in the matrix; and based on detecting the second interaction,modifying the graphical user interface by: displaying a first matrix ofblocks within the graphical user interface, the first matrix of blocksbeing color coded to represent weights of a filter used to generate afeature-map value indicated by the symbol; and displaying a secondmatrix of blocks within the graphical user interface, the second matrixof blocks being color coded to represent values in a convolutionalmatrix formed by multiplying the weights of the filter by embeddingweights corresponding to the symbol.
 15. The method of claim 14,wherein: the clustering method includes a k-means clustering method; thefirst matrix of blocks is positioned above and adjacent to the secondmatrix of blocks; the first matrix of blocks and the second matrix ofblocks are positioned to a right of the matrix that has the rows andcolumns of symbols; and the symbols in the matrix are color coded torepresent the feature-map values.
 16. The method of claim 11, whereinthe interaction is a first interaction, and further comprising:detecting a second interaction with the graphical user interface, thesecond interaction indicating a threshold value for compressing the rowsin the matrix; and based on detecting the second interaction, displayinga compressed version of the matrix by: determining that adjacent rows inthe matrix have a Euclidian distance that is below the threshold value;and merging the adjacent rows into a single row in the matrix, whereinthe single row has symbols with visual characteristics that representaverages of the feature-map values represented by the adjacent rows. 17.The method of claim 16, further comprising: detecting a thirdinteraction with the graphical user interface, the third interactionindicating that a group of rows represented by a single row in thecompressed version of the matrix is to be expanded; and based ondetecting the third interaction, displaying an expanded version of thematrix by visually displaying all of the rows in the group of rows. 18.The method of claim 11, further comprising including a node-link diagramwithin the graphical user interface, wherein the node-link diagramincludes: a first row of symbols representing an input layer to a feedforward neural network that is part of the convolutional neural network,the first row of symbols having visual characteristics representative ofvalues at the input layer; one or more rows of symbols representing oneor more hidden layers of the feed forward neural network, the one ormore rows of symbols having visual characteristics representative ofvalues at the one or more hidden layers; a final row of symbolsrepresenting an output layer of the feed forward neural network, thefinal row of symbols having visual characteristics representative ofvalues at the output layer; and lines between (i) the first row ofsymbols representing the input layer, (ii) the one or more rows ofsymbols representing the one or more hidden layers, and (ii) the finalrow of symbols representing the output layer, wherein the linesrepresent connections between the input layer, the one or more hiddenlayers, and the output layer.
 19. The method of claim 18, furthercomprising: detecting a second interaction indicating that a layer ofthe feed forward neural network is to be deactivated in the graphicaluser interface; and based on detecting the second interaction: visuallyrepresenting the layer of the feed forward neural network as a line ofblocks, each block representing a node in the layer of the feed forwardneural network and being color coded to represent an activation value ofthe node; and visually hiding at least a portion of the lines thatrepresent connections between the layer of the feed forward neuralnetwork and an adjacent layer of the feed forward neural network. 20.The method of claim 19, further comprising: determining that theadjacent layer is also to be deactivated in the graphical userinterface; and based on determining that the adjacent layer is also tobe deactivated in the graphical user interface, visually hiding all ofthe lines that represent connections between the layer and an adjacentlayer.
 21. A non-transitory computer-readable medium comprising programcode that is executable by a processing device for causing theprocessing device to: display, via a display device, a graphical userinterface comprising a matrix having rows and columns of symbolsindicating feature-map values that represent likelihoods of particularfeatures being present or absent at various locations in an input to aconvolutional neural network, each column in the matrix havingfeature-map values generated by convolving the input to theconvolutional neural network with a respective filter for identifying aparticular feature in the input; detect, via an input device, aninteraction with the graphical user interface indicating that thecolumns in the matrix are to be combined into a particular number ofgroups; and in response to detecting the interaction: cluster thecolumns into the particular number of groups using a clustering method;for each group of columns, determine a plurality of average feature-mapvalues, each average feature-map value being determined by averaging thefeature-map values represented in a respective row of the columns; anddisplay an updated version of the matrix within the graphical userinterface by visually representing each respective group of columns as asingle column of symbols within the matrix, each symbol in the singlecolumn of symbols having visual characteristics representing an averagefeature-map value, from among the plurality of average feature-mapvalues, that corresponds to a row in which the symbol is positioned. 22.The non-transitory computer-readable medium of claim 21, wherein theinteraction is a first interaction, and further comprising program codethat is executable by the processing device for causing the processingdevice to: detect a second interaction with the graphical userinterface, the second interaction indicating that a group of columnsrepresented by a single column in the updated version of the matrix isto be expanded; and based on detecting the second interaction, displayan expanded version of the matrix that visually includes all of thecolumns in the group of columns.
 23. The non-transitorycomputer-readable medium of claim 22, further comprising program codethat is executable by the processing device for causing the processingdevice to: detect a third interaction indicating that the expandedversion of the matrix is to be contracted; and based on detecting thethird interaction, display a contracted version of the matrix in whichthe group of columns are merged into the single column.
 24. Thenon-transitory computer-readable medium of claim 21, wherein theinteraction is a first interaction, and further comprising program codethat is executable by the processing device for causing the processingdevice to: detect a second interaction with the graphical userinterface, the second interaction comprising hovering a cursor over asymbol in the matrix; and based on detecting the second interaction,modify the graphical user interface by: displaying a first matrix ofblocks within the graphical user interface, the first matrix of blocksbeing color coded to represent weights of a filter used to generate afeature-map value indicated by the symbol; and displaying a secondmatrix of blocks within the graphical user interface, the second matrixof blocks being color coded to represent values in a convolutionalmatrix formed by multiplying the weights of the filter by embeddingweights corresponding to the symbol.
 25. The non-transitorycomputer-readable medium of claim 24, wherein: the clustering methodincludes a k-means clustering method; the first matrix of blocks ispositioned above and adjacent to the second matrix of blocks; the firstmatrix of blocks and the second matrix of blocks are positioned to aright of the matrix that has the rows and columns of symbols; and thesymbols in the matrix are color coded to represent the feature-mapvalues.
 26. The non-transitory computer-readable medium of claim 21,wherein the interaction is a first interaction, and further comprisingprogram code that is executable by the processing device for causing theprocessing device to: detect a second interaction with the graphicaluser interface, the second interaction indicating a threshold value forcompressing the rows in the matrix; and based on detecting the secondinteraction, display a compressed version of the matrix by: determiningthat adjacent rows in the matrix have a Euclidian distance that is belowthe threshold value; and merging the adjacent rows into a single row inthe matrix, wherein the single row has symbols with visualcharacteristics that represent averages of the feature-map valuesrepresented by the adjacent rows.
 27. The non-transitorycomputer-readable medium of claim 26, further comprising program codethat is executable by the processing device for causing the processingdevice to: detect a third interaction with the graphical user interface,the third interaction indicating that a group of rows represented by asingle row in the compressed version of the matrix is to be expanded;and based on detecting the third interaction, display an expandedversion of the matrix by visually displaying all of the rows in thegroup of rows.
 28. The non-transitory computer-readable medium of claim21, further comprising program code that is executable by the processingdevice for causing the processing device to include a node-link diagramwithin the graphical user interface, wherein the node-link diagramincludes: a first row of symbols representing an input layer to a feedforward neural network that is part of the convolutional neural network,the first row of symbols having visual characteristics representative ofvalues at the input layer; one or more rows of symbols representing oneor more hidden layers of the feed forward neural network, the one ormore rows of symbols having visual characteristics representative ofvalues at the one or more hidden layers; a final row of symbolsrepresenting an output layer of the feed forward neural network, thefinal row of symbols having visual characteristics representative ofvalues at the output layer; and lines between (i) the first row ofsymbols representing the input layer, (ii) the one or more rows ofsymbols representing the one or more hidden layers, and (ii) the finalrow of symbols representing the output layer, wherein the linesrepresent connections between the input layer, the one or more hiddenlayers, and the output layer.
 29. The non-transitory computer-readablemedium of claim 28, further comprising program code that is executableby the processing device for causing the processing device to: detect asecond interaction indicating that a layer of the feed forward neuralnetwork is to be deactivated in the graphical user interface; and basedon detecting the second interaction: visually represent the layer of thefeed forward neural network as a line of blocks, each block representinga node in the layer of the feed forward neural network and being colorcoded to represent an activation value of the node; and visually hide atleast a portion of the lines that represent connections between thelayer of the feed forward neural network and an adjacent layer of thefeed forward neural network.
 30. The non-transitory computer-readablemedium of claim 29, further comprising program code that is executableby the processing device for causing the processing device to: determinethat the adjacent layer is also to be deactivated in the graphical userinterface; and based on determining that the adjacent layer is also tobe deactivated in the graphical user interface, visually hide all of thelines that represent connections between the layer and an adjacentlayer.